Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the “6S” goals of parallel driving.

Due to the breaking of time-reversal and parity symmetries and the presence of non-conservative microscopic interactions, active spinner fluids and solids respectively exhibit nondissipative odd viscosity and nonstorage odd elasticity, engendering phenomena unattainable in traditional passive or active systems. Here, we study the effects of odd viscosity and elasticity on phase behaviors of active spinner systems. We find the spinner fluid under a simple shear experiences an anisotropic gas–liquid phase separation driven by the odd-viscosity stress. This phase separation exhibits equilibrium-like behavior, with both binodal-like and spinodal curves and critical point. However, the formed dense liquid phase is unstable, since the odd elasticity instantly takes over the odd viscosity to condense the liquid into a solid-like phase. The unusual phase behavior essentially arises from the competition between thermal fluctuations and the odd response-induced effective attraction. Our results demonstrate that the cooperation of odd viscosity and elasticity can lead to exotic phase behavior, revealing their fundamental roles in phase transition.

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.

Helicobacter pylori colonizes over 50% of people worldwide. Biofilm formation through penetrating gastric mucus and resistance acquired by H. pylori markedly reduces the efficacy of traditional antibiotics. The present triple therapy and bismuth-based quadruple therapy inevitably causes intestinal flora disturbance and fails to address the excessive H. pylori-triggered inflammatory response. Herein, a mucus-permeable therapeutic platform (Cu-MOF@NF) that consists of copper-bearing metal-organic framework (Cu-MOF) loaded with nitrogen-doped carbon dots and naturally active polysaccharide fucoidan is developed. The experimental results demonstrate that Cu-MOF@NF can penetrate the mucus layer and hinder H. pylori from adhering on gastric epithelial cells of the stomach. Notably, released Cu²⁺ can degrade the polysaccharides in the biofilm and interfere with the cyclic growing mode of “bacterioplankton ↔ biofilm”, thereby preventing recurrent and persistent infection. Compared with traditional triple therapy, the Cu-MOF@NF not only possesses impressive antibacterial effect (even include multidrug-resistant strains), but also improves the inflammatory microenvironment without disrupting the balance of intestinal flora, providing a more efficient, safe, and antibiotic-free new approach to eradicating H. pylori.

Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (MAE) and an increase of 79.95% in signal-to-noise ratio (SNR). Regarding HR estimation accuracy, FM-FCN achieves a decrease of 35.85% in MAE, 29.65% in error standard deviation, and 32.88% decrease in 95% limits of agreement width, meeting clinical standards for HR accuracy requirements. The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction. The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.

Recent work demonstrated stimulated Raman adiabatic passage-type transfer of energy along 3 acoustic cavities. After brief comments on the stimulated Raman adiabatic passage method, remarks on the scientific and technological relevance of this work are presented, followed by noting other recent important applications of the process.

The intestinal and intratumoral microbiota are closely associated with tumor progression and response to antitumor treatments. The antibacterial or tumor microenvironment (TME)-modulating approaches have been shown to markedly improve antitumor efficacy, strategies focused on normalizing the microbial environment are rarely reported. Here, we reported the development of an orally administered inulin-based hydrogel with colon-targeting and retention effects, containing hollow MnO₂ nanocarrier loaded with the chemotherapeutic drug Oxa (Oxa@HMI). On the one hand, beneficial bacteria in the colon specifically metabolized Oxa@HMI, resulting in the degradation of inulin and the generation of short-chain fatty acids (SCFAs). These SCFAs play a crucial role in modulating microbiota and stimulating immune responses. On the other hand, the hydrogel matrix underwent colon microbiota-specific degradation, enabling the targeted release of Oxa and production of reactive oxygen species in the acidic TME. In this study, we have established, for the first time, a microbiota-targeted drug delivery system Oxa@HMI that exhibited high efficiency in colorectal cancer targeting and colon retention. Oxa@HMI promoted chemotherapy efficiency and activated antitumor immune responses by intervening in the microbial environment within the tumor tissue, providing a crucial clinical approach for the treatment of colorectal cancer that susceptible to microbial invasion.

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.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

Despite the recognized influence of natural factors on groundwater, the impact of human activities remains less explored because of the challenges in measuring such effects. To address this gap, our study proposes an approach that considers carbon emissions as an indicator of human activity intensity and quantifies their impact on groundwater storage. The combination of carbon emission data and groundwater storage data for 17,152 grid cells over 16 years in 4 typical basins shows that they were generally negatively correlated, whereas both agriculture and aviation had positive impacts on groundwater storage. The longest impact from aviation and agriculture can even persist for 7 years. Furthermore, an increase of 1 Yg CO₂/km² per second in emissions from petroleum processing demonstrates the most pronounced loss of groundwater storage in the Yangtze River Basin (approximately 4.1 mm). Moreover, regions characterized by high-quality economic development tend to have favorable conditions for groundwater storage. Overall, our findings revealed the substantial role of human activities in influencing groundwater dynamics from both temporal and spatial aspects. This study fills a crucial gap by exploring the relationship between human activities and groundwater storage through the introduction of a quantitative modeling framework based on carbon emissions. It also provides insights for facilitating empirical groundwater management planning and achieving optimal emission reduction levels.

Poly (adenosine 5′-diphosphate-ribose) polymerase inhibitors (PARPi) are increasingly important in the treatment of ovarian cancer. However, more than 40% of BRCA1/2-deficient patients do not respond to PARPi, and BRCA wild-type cases do not show obvious benefit. In this study, we demonstrated that progesterone acted synergistically with niraparib in ovarian cancer cells by enhancing niraparib-mediated DNA damage and death regardless of BRCA status. This synergy was validated in an ovarian cancer organoid model and in vivo experiments. Furthermore, we found that progesterone enhances the activity of niraparib in ovarian cancer through inducing ferroptosis by up-regulating palmitoleic acid and causing mitochondrial damage. In clinical cohort, it was observed that progesterone prolonged the survival of patients with ovarian cancer receiving PARPi as second-line maintenance therapy, and high progesterone receptor expression combined with low glutathione peroxidase 4 (GPX4) expression predicted better efficacy of PARPi in patients with ovarian cancer. These findings not only offer new therapeutic strategies for PARPi poor response ovarian cancer but also provide potential molecular markers for predicting the PARPi efficacy.

Circular aptamers are promising candidates for analytical and therapeutic applications due to their enhanced biological and structural stability. However, the process of circular aptamer selection remains a great challenge, as it requires multiple rounds of binding–separation–amplification that involves issues with nonspecific binding and amplification bias. Here, we develop a highly practical solution for reliable selection of circular aptamers in a single round based on magnetosome-like magnetic chain cross-linked graphene oxide (separation efficiency ≈ 10⁵). High-affinity aptamer candidates can be rapidly selected from a preenriched circular DNA library, while low-affinity candidates are effectively adsorbed and separated by magnetosome-like magnetic chain cross-linked graphene oxide. With lipopolysaccharide as a representative model, the single-round selected lipopolysaccharide circular aptamer has been identified to have a high binding affinity with a K_d value of low to nanomolar range. Using this method, circular aptamers for protein and small-molecule targets were also successfully generated. We envision that this approach will accelerate the discovery of various new circular aptamers and open up a new avenue for analytical and therapeutic studies.

Gout, a common form of arthritis, is characterized by the deposition of monosodium urate (MSU) crystals in joints. MSU deposition in synovial tissues would initiate arthritis flares and recurrence, causing irreversible joint damage. However, the dynamic deposition of MSU crystals in tissues lacks experimental observation. In this study, we used chemical-specific, label-free stimulated Raman scattering (SRS) microscopy to investigate the spatiotemporal deposition and morphological characteristics of MSU crystals in human synovial organoids. Our findings revealed a critical 12-h window for MSU deposition in the lining layer of gouty synovium. Moreover, distinctive inflammatory reactions of the lining and sublining synovial layers in gout using SRS microscopy were further verified by immunofluorescence. Importantly, we identified a crucial proinflammatory role of sublining fibroblast-like synoviocytes, indicating a need for targeted medication treatment on these cells. Our work contributes to the fundamental understanding of MSU-based diseases and offers valuable insights for the future development of targeted gout therapies.

Commensal enterococci with pathogenic potential often facilitate the growth of diverse pathogens, thereby exacerbating infections. However, there are few effective therapeutic strategies to prevent and intervene in enterococci-mediated polymicrobial infections. Here, we find that enterococci at high density drive the expansion and pathogenicity of enteric Salmonella enterica serotype Typhimurium (S. Tm). Subsequently, we show that the driving role of enterococci in such infections is counteracted by dietary coumarin glycosides in vivo. Enterococci, which are tolerant of iron-deficient environments, produce β-glucosidases to hydrolyze coumarin glycosides into bioactive aglycones, inhibiting S. Tm growth and ameliorating the severity of S. Tm-induced symptoms by inducing iron limitation. Overall, we demonstrate that coumarin glycosides as a common diet effectively reverse enterococci-facilitated enteric infections, providing an alternative intervention to combat polymicrobial infections.

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.

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.

4,4-Dimethylsterols constitute a unique class of phytosterols responsible for regulating endogenous cannabinoid system (ECS) functions. However, precise mechanism through which 4,4-dimethylsterols affect fat metabolism and the linkage to the ECS remain unresolved. In this study, we identified that 4,4-dimethylsterols, distinct from 4-demethseterols, act as inhibitors of fatty acid amide hydrolases (FAAHs) both in vivo and in vitro. Genetic ablation of FAAHs (faah-1) abolishes the effects of 4,4-dimethylsterols on fat accumulation and locomotion behavior in a Caenorhabditis elegans model. We confirmed that dietary intervention with 4,4-dimethylsterols in a high-fat diet (HFD) mouse model leads to a significant reduction in body weight (>11.28%) with improved lipid profiles in the liver and adipose tissues and increased fecal triacylglycerol excretion. Untargeted and targeted metabolomics further verified that 4,4-dimethylsterols influence unsaturated fatty acid biosynthesis and elevate oleoyl ethanolamine levels in the intestine. We propose a potential molecular mechanism in which 4,4-dimethylsterols engage in binding interactions with the catalytic pocket (Ser241) of FAAH-1 protein due to the shielded polarity, arising from the presence of 2 additional methyl groups (CH₃). Consequently, 4,4-dimethylsterols represent an unexplored class of beneficial phytosterols that coordinate with FAAH-1 activity to reduce fat accumulation, which offers new insight into intervention strategies for treating diet-induced obesity.

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.

Cement-based materials are the foundation of modern buildings but suffer from intensive energy consumption. Utilizing cement-based materials for efficient energy storage is one of the most promising strategies for realizing zero-energy buildings. However, cement-based materials encounter challenges in achieving excellent electrochemical performance without compromising mechanical properties. Here, we introduce a biomimetic cement-based solid-state electrolyte (labeled as l-CPSSE) with artificially organized layered microstructures by proposing an in situ ice-templating strategy upon the cement hydration, in which the layered micropores are further filled with fast-ion-conducting hydrogels and serve as ion diffusion highways. With these merits, the obtained l-CPSSE not only presents marked specific bending and compressive strength (2.2 and 1.2 times that of traditional cement, respectively) but also exhibits excellent ionic conductivity (27.8 mS·cm⁻¹), overwhelming most previously reported cement-based and hydrogel-based electrolytes. As a proof-of-concept demonstration, we assemble the l-CPSSE electrolytes with cement-based electrodes to achieve all-cement-based solid-state energy storage devices, delivering an outstanding full-cell specific capacity of 72.2 mF·cm⁻². More importantly, a 5 × 5 cm² sized building model is successfully fabricated and operated by connecting 4 l-CPSSE-based full cells in series, showcasing its great potential in self-energy-storage buildings. This work provides a general methodology for preparing revolutionary cement-based electrolytes and may pave the way for achieving zero-carbon buildings.

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.

Metabolic dysfunction-associated steatohepatitis (MASH) is the progressive form of metabolic dysfunction-associated steatotic liver disease (MASLD), and closely associated with a high risk of liver-related morbidity and mortality. Although enhanced neutrophil infiltration of the liver is a histological hallmark of MASH, the morphological pattern of hepatic neutrophils and their relevance to the definition of MASH remain unknown. This clinicopathological study aimed to determine the association of neutrophilic crown-like structures (CLSs) in liver biopsies and evaluate their relevance to the histological diagnosis of MASH. A total of 483 morbidly obese adults who underwent bariatric surgery were recruited. Neutrophilic CLSs in liver biopsies were detected by immunohistochemistry for neutrophil elastase and proteinase 3. All participants were classified into 4 histological subgroups: no MASLD (118, 24.4%), MASLD (76, 15.7%), borderline MASH (185, 38.3%), and definite MASH (104, 21.5%). In the discovery cohort (n = 379), the frequency of neutrophilic CLSs increased in line with the severity of liver disease. The number of neutrophilic CLSs was positively correlated with established histological characteristics of MASH. At a cutoff value of <0.3 per 20× microscopic field, the number of neutrophilic CLSs yielded a robust diagnostic accuracy to discriminate no MASLD and MASLD from borderline MASH and definite MASH; a cutoff at >1.3 per 20× microscopic field exhibited a statistically significant accuracy to distinguish definite MASH from other groups (no MASLD, MASLD, and borderline MASH). The significance of neutrophilic CLSs in identifying borderline MASH and definite MASH was confirmed in an external validation cohort (n = 104). The frequency of neutrophilic CLSs was significantly higher than that of macrophagic CLSs. In conclusion, neutrophilic CLSs in the liver represent a typical histological characteristic of MASH and may serve as a promising indicator to improve the diagnostic accuracy of MASH during histological assessment of liver biopsies.

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.

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.

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.

Thrombosis can cause life-threatening disorders. Unfortunately, current therapeutic methods for thrombosis using injecting thrombolytic medicines systemically resulted in unexpected bleeding complications. Moreover, the absence of practical imaging tools for thrombi raised dangers of undertreatment and overtreatment. This study develops a theranostic drug carrier, Pkr(IR-Ca/Pda-uPA)-cRGD, that enables real-time monitoring of the targeted thrombolytic process of deep vein thrombosis (DVT). Pkr(IR-Ca/Pda-uPA)-cRGD, which is prepared from a Pickering-emulsion-like system, encapsulates both near-infrared-II (NIR-II) contrast agent (IR-1048 dye, loading capacity: 28%) and urokinase plasminogen activators (uPAs, encapsulation efficiency: 89%), pioneering the loading of multiple drugs with contrasting hydrophilicity into one single-drug carrier. Upon intravenous injection, Pkr(IR-Ca/Pda-uPA)-cRGD considerably targets to thrombi selectively (targeting rate: 91%) and disintegrates in response to acidic thrombi to release IR-1048 dye and uPA for imaging and thrombolysis, respectively. Investigations indicate that Pkr(IR-Ca/Pda-uPA)-cRGD enabled real-time visualization of targeted thrombolysis using NIR-II imaging in DVT models, in which thrombi were eliminated (120 min after drug injection) without bleeding complications. This may be the first study using convenient NIR-II imaging for real-time visualization of targeted thrombolysis. It represents the precision medicine that enables rapid response to acquire instantaneous medical images and make necessary real-time adjustments to diagnostic and therapeutic protocols during treatment.

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand–receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

One of the fundamental principles of electrostatics is that an uncharged object will be attracted to a charged object through electrostatic induction as the two approaches one another. We refer to the charged object as a single electrode and examine the scenario where a positive voltage is applied. Because of electrostatic induction phenomenon, single-electrode electrostatics only generates electrostatic attraction forces. Here, we discover that single-electrode electrostatics can generate electrostatic repulsion forces and define this new phenomenon as single-electrode electrostatic repulsion phenomenon. We investigate the fundamental electrostatic phenomena, giving a curve of electrostatic force versus voltage and then defining 3 regions. Remote actuation and manipulation are essential technologies that are of enormous concern, with tweezers playing an important role. Various tweezers designed on the basis of external fields of optics, acoustics, and magnetism can be used for remote actuation and manipulation, but some inherent drawbacks still exist. Tweezers would benefit greatly from our discovery in electrostatics. On the basis of this discovery, we propose the concept of electrostatic tweezers, which can achieve noncontact and remote actuation and manipulation. Experimental characterizations and successful applications in metamaterials, robots, and manipulating objects demonstrated that electrostatic tweezers can produce large deformation rates (>6,000%), fast actuation (>100 Hz), and remote manipulating distance (~15 cm) and have the advantages of simple device structure, easy control, lightweight, no dielectric breakdown, and low cost. Our work may deepen people's understanding of single-electrode electrostatics and opens new opportunities for remote actuation and manipulation.