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.

Resistin plays an important role in the pathophysiology of obesity-mediated insulin resistance in mice. However, the biology of resistin in humans is quite different from that in rodents. Therefore, the association between resistin and insulin resistance remains unclear in humans. Here, we tested whether and how the endocannabinoid system (ECS) control circulating peripheral blood mononuclear cells (PBMCs) that produce resistin and infiltrate into the adipose tissue, heart, skeletal muscle, and liver, resulting in inflammation and insulin resistance. Using human PBMCs, we investigate whether the ECS is connected to human resistin. To test whether the ECS regulates inflammation and insulin resistance in vivo, we used 2 animal models such as “humanized” nonobese diabetic/Shi-severe combined immunodeficient interleukin-2Rγ (null) (NOG) mice and “humanized” resistin mouse models, which mimic human body. In human atheromatous plaques, cannabinoid 1 receptor (CB1R)-positive macrophage was colocalized with the resistin expression. In addition, resistin was exclusively expressed in the sorted CB1R-positive cells from human PBMCs. In CB1R-positive cells, endocannabinoid ligands induced resistin expression via the p38–Sp1 pathway. In both mouse models, a high-fat diet increased the accumulation of endocannabinoid ligands in adipose tissue, which recruited the CB1R-positive cells that secrete resistin, leading to adipose tissue inflammation and insulin resistance. This phenomenon was suppressed by CB1R blockade or in resistin knockout mice. Interestingly, this process was accompanied by mitochondrial change that was induced by resistin treatment. These results provide important insights into the ECS–resistin axis, leading to the development of metabolic diseases. Therefore, the regulation of resistin via the CB1R could be a potential therapeutic strategy for cardiometabolic diseases.

In the evolving landscape of robotics and visual navigation, event cameras have gained important traction, notably for their exceptional dynamic range, efficient power consumption, and low latency. Despite these advantages, conventional processing methods oversimplify the data into 2 dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we have developed a 3D spatiotemporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on population activity. This ensures adaptability and precision under various conditions, like changes in motion velocity and ambient lighting. In our novel validation approach, we first identify the noise type and determine its power spectral density in the event stream. We then apply a one-dimensional discrete fast Fourier transform to assess the filtered event data within the frequency domain, ensuring that the targeted noise frequencies are adequately reduced. Our research also delved into the impact of indoor lighting on event stream noise. Remarkably, our method led to a 37% decrease in the data point cloud, improving data quality in diverse outdoor settings.

The methylation of adenosine base at the nitrogen-6 position is referred to as “N6-methyladenosine (m⁶A)” and is one of the most prevalent epigenetic modifications in eukaryotic mRNA and noncoding RNA (ncRNA). Various m⁶A complex components known as “writers,” “erasers,” and “readers” are involved in the function of m⁶A. Numerous studies have demonstrated that m⁶A plays a crucial role in facilitating communication between different cell types, hence influencing the progression of diverse physiological and pathological phenomena. In recent years, a multitude of functions and molecular pathways linked to m⁶A have been identified in the osteogenic, adipogenic, and chondrogenic differentiation of bone mesenchymal stem cells (BMSCs). Nevertheless, a comprehensive summary of these findings has yet to be provided. In this review, we primarily examined the m⁶A alteration of transcripts associated with transcription factors (TFs), as well as other crucial genes and pathways that are involved in the differentiation of BMSCs. Meanwhile, the mutual interactive network between m⁶A modification, miRNAs, and lncRNAs was intensively elucidated. In the last section, given the beneficial effect of m⁶A modification in osteogenesis and chondrogenesis of BMSCs, we expounded upon the potential utility of m⁶A-related therapeutic interventions in the identification and management of human musculoskeletal disorders manifesting bone and cartilage destruction, such as osteoporosis, osteomyelitis, osteoarthritis, and bone defect.

Adeno-associated virus (AAV)-mediated gene therapy is widely applied to treat numerous hereditary diseases in animal models and humans. The specific expression of AAV-delivered transgenes driven by cell type-specific promoters should further increase the safety of gene therapy. However, current methods for screening cell type-specific promoters are labor-intensive and time-consuming. Herein, we designed a “multiple vectors in one AAV” strategy for promoter construction in vivo. Through this strategy, we truncated a native promoter for Myo15 expression in hair cells (HCs) in the inner ear, from 1,611 bp down to 1,157 bp, and further down to 956 bp. Under the control of these 2 promoters, green fluorescent protein packaged in AAV-PHP.eB was exclusively expressed in the HCs. The transcription initiation ability of the 2 promoters was further verified by intein-mediated otoferlin recombination in a dual-AAV therapeutic system. Driven by these 2 promoters, human otoferlin was selectively expressed in HCs, resulting in the restoration of hearing in treated Otof ^−/− mice for at least 52 weeks. In summary, we developed an efficient screening strategy for cell type-specific promoter engineering and created 2 truncated Myo15 promoters that not only restored hereditary deafness in animal models but also show great potential for treating human patients in future.