Search

Abstract Dysfunction of pancreatic δ cells contributes to the etiology of diabetes. Despite their important role, human δ cells are scarce, limiting physiological studies and drug discovery targeting δ cells. To date, no directed δ-cell differentiation method has been established. Here, we demonstrate that fibroblast growth factor (FGF) 7 promotes pancreatic endoderm/progenitor differentiation, whereas FGF2 biases cells towards the pancreatic δ-cell lineage via FGF receptor 1. We develop a differentiation method to generate δ cells from human stem cells by combining FGF2 with FGF7, which synergistically directs pancreatic lineage differentiation and modulates the expression of transcription factors and SST activators during endoderm/endocrine precursor induction. These δ cells display mature RNA profiles and fine secretory granules, secrete somatostatin in response to various stimuli, and suppress insulin secretion from in vitro co-cultured β cells and mouse β cells upon transplantation. The generation of human pancreatic δ cells from stem cells in vitro would provide an unprecedented cell source for drug discovery and cell transplantation studies in diabetes.

Abstract Triple negative breast cancer (TNBC) has the characteristics of low immune cell infiltration, high expression of tumor programmed death ligand 1 (PD-L1), and abundant cancer stem cells. Systemic toxicity of traditional chemotherapy drugs due to poor drug selectivity, and chemotherapy failure due to tumor drug resistance and other problems, so it is particularly important to find new cancer treatment strategies for TNBC with limited treatment options. Both the anti-tumor natural drugs curcumin and ginsenoside Rg3 can exert anti-tumor effects by inducing immunogenic cell death (ICD) of tumor cells, reducing PD-L1 expression, and reducing cancer stem cells. However, they have the disadvantages of poor water solubility, low bioavailability, and weak anti-tumor effect of single agents. We used vinyl ether bonds to link curcumin (Cur) with N-O type zwitterionic polymers and at the same time encapsulated ginsenoside Rg3 to obtain hyperbranched zwitterionic drug-loaded micelles OPDEA-PGED-5HA@Cur@Rg3 (PPH@CR) with pH response. In vitro cell experiments and in vivo animal experiments have proved that PPH@CR could not only promote the maturation of dendritic cells (DCs) and increase the CD4+ T cells and CD8+ T cells by inducing ICD in tumor cells but also reduce the expression of PD-L1 in tumor tissues, and reduce cancer stem cells and showed better anti-tumor effects and good biological safety compared with free double drugs, which is a promising cancer treatment strategy.

Abstract Develop a radiomics nomogram that integrates deep learning, radiomics, and clinical variables to predict epidermal growth factor receptor (EGFR) mutation status in patients with stage I non-small cell lung cancer (NSCLC). We retrospectively included 438 patients who underwent curative surgery and completed driver-gene mutation tests for stage I NSCLC from four academic medical centers. Predictive models were established by extracting and analyzing radiomic features in intratumoral, peritumoral, and habitat regions of CT images to identify EGFR mutation status in stage I NSCLC. Additionally, three deep learning models based on the intratumoral region were constructed. A nomogram was developed by integrating representative radiomic signatures, deep learning, and clinical features. Model performance was assessed by calculating the area under the receiver operating characteristic (ROC) curve. The established habitat radiomics features demonstrated encouraging performance in discriminating between EGFR mutant and wild-type, with predictive ability superior to other single models (AUC 0.886, 0.812, and 0.790 for the training, validation, and external test sets, respectively). The radiomics-based nomogram exhibited excellent performance, achieving the highest AUC values of 0.917, 0.837, and 0.809 in the training, validation, and external test sets, respectively. Decision curve analysis (DCA) indicated that the nomogram provided a higher net benefit than other radiomics models, offering valuable information for treatment.

Persistent inflammatory responses often occur when bacteria and other microorganisms frequently invade and colonize open wounds and eventually result in the formation of chronic wounds. Therefore, achieving real-time detection of invasive bacteria accurately and promptly is essential for efficient wound management and accelerating the healing process. Recently, flexible wearable sensors have garnered significant attention, especially those designed for monitoring real-time biophysical or biochemical signals in wound sites in a minimally invasive manner. They provide more precise and continuous monitoring data, making them as emerging tools for clinical diagnostics. In this review, we first discuss the species and community distribution of different types of bacteria in chronic wounds. Next, we introduce currently developed techniques for detecting bacteria at wound sites. Following that, we discuss the recent progress and unresolved issues of various flexible wearable sensors in detecting bacteria at wound sites. We believe that this review can provide meaningful guidance for the development of flexible wearable sensors for bacteria detection.

Abstract Background Bladder cancer (BC) is among the most prevalent malignant urothelial tumors globally, yet the prognosis for patients with muscle-invasive bladder cancer (MIBC) remains dismal, with a very poor 5-year survival rate. Consequently, identifying more effective and less toxic chemotherapeutic alternatives is critical for enhancing clinical outcomes for BC patients. Isorhapontigenin (ISO), a novel stilbene isolated from a Gnetum found in certain provinces of China, has shown potential as an anticancer agent due to its diverse anticancer activities. Despite its promising profile, the specific anticancer effects of ISO on BC and the underlying mechanisms are still largely unexplored. Methods The anchorage-independent growth, migration and invasion of BC cells were assessed by soft agar and transwell invasion assays, respectively. The RNA levels of SOX2, miR-129 and SNHG1 were quantified by qRT-PCR, while the protein expression levels were validated through Western blotting. Furthermore, methylation-specific PCR was employed to assess the methylation status of the miR-129 promoter. Functional assays utilized siRNA knockdown, plasmid-mediated overexpression, and chemical inhibition approaches. Results Our study demonstrated that ISO treatment significantly reduced SNHG1 expression in a dose- and time-dependent manner in BC cells, leading to the inhibition of anchorage-independent growth and invasion in human basal MIBC cells. This effect was accompanied by the downregulation of MMP-2 and MMP-9 and the upregulation of the tumor suppressor PTEN. Further mechanistic investigations revealed that SOX2, a key upstream regulator of SNHG1, played a crucial role in mediating the ISO-induced transcriptional suppression of SNHG1. Additionally, we found that ISO treatment led to a decrease in DNMT3b protein levels, which in turn mediated the hypomethylation of the miR-129 promoter and the subsequent suppression of SOX2 mRNA 3’-UTR activity, highlighting a novel pathway through which ISO exerts its anticancer effects. Conclusions Collectively, our study highlights the critical role of SNHG1 downregulation as well as its upstream DNMT3b/miR-129/SOX2 axis in mediating ISO anticancer activity. These findings not only elucidate the mechanism of action of ISO but also suggest novel targets for BC therapy.

The microstructure, texture, and mechanical properties of as-extruded and cold-rolled Mg–8Li–3Al–2Zn-1Gd-0.2Zr (LAZ832-1Gd-0.2Zr) alloys were investigated with the aim of strengthening the alloy by combining hot extrusion and cold rolling. Cold rolling does not change the phase composition of as-extruded LAZ832-1Gd-0.2Zr alloy, but introduces a larger number of dislocations and twins into alloy matrix. Cold rolling leads to the weakening of the original {0001} basal and {112‾0}-{011‾0} prismatic textures in α-Mg matrix, as well as the α and γ fiber textures in β-Li matrix. But new slip systems that are favorable for cold rolling are activated, resulting in the formation of {033‾8} pyramidal and new prismatic ({112‾0}⟨101‾4⟩ and {101‾0}⟨21‾1‾10⟩) texture components. Cold rolling further strengthens LAZ832-1Gd-0.2Zr alloy on the basis of hot extrusion, mainly through work hardening effect caused by introducing a large number of dislocations into α-Mg matrix. When the rolling reduction is 45%, the yield strength and tensile strength of LAZ832-1Gd-0.2Zr alloy are increased to 269.8 ± 4.0 and 308.6 ± 4.1 MPa, respectively, and the elongation is higher than 15%.

Abstract In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving.

Abstract The dry reforming of methane provides an attractive route to convert greenhouse gases (CH4 and CO2) into valuable syngas, so as to resolve the carbon cycle and environmental issues. However, the development of high-performance catalysts remains a huge challenge. Herein, we report a 0.6% Ir/CeO2−x catalyst with a metal-support interface structure which exhibits high CH4 (~72%) and CO2 (~82%) conversion and a CH4 reaction rate of ~973 μmolCH4 gcat −1 s−1 which is stable over 100 h at 700 °C. The performance of the catalyst is close to the state-of-the-art in this area of research. A combination of in situ spectroscopic characterization and theoretical calculations highlight the importance of the interfacial structure as an intrinsic active center to facilitate the CH4 dissociation (the rate-determining step) and the CH2* oxidation to CH2O* without coke formation, which accounts for the long-term stability. The catalyst in this work has a potential application prospect in the field of high-value utilization of carbon resources.

Abstract The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strategy against COVID-19. In the current study, we utilized islet organoids derived from human embryonic stem cells (hESCs), animal models and COVID-19 patients to discover that fibroblast growth factor 7 (FGF7) enhances ACE2 expression within the islets, facilitating SARS-CoV-2 infection and resulting in impaired insulin secretion. Using hESC-derived islet organoids, we demonstrated that FGF7 interacts with FGF receptor 2 (FGFR2) and FGFR1 to upregulate ACE2 expression predominantly in β cells. This upregulation increases both insulin secretion and susceptibility of β cells to SARS-CoV-2 infection. Inhibiting FGFR counteracts the FGF7-induced ACE2 upregulation, subsequently reducing viral infection and replication in the islets. Furthermore, retrospective clinical data revealed that diabetic patients with severe COVID-19 symptoms exhibited elevated serum FGF7 levels compared to those with mild symptoms. Finally, animal experiments indicated that SARS-CoV-2 infection increased pancreatic FGF7 levels, resulting in a reduction of insulin concentrations in situ. Taken together, our research offers a potential regulatory strategy for ACE2 by controlling FGF7, thereby protecting islets from SARS-CoV-2 infection and preventing the progression of diabetes in the context of COVID-19.

Due to the detrimental effect of formaldehyde on DNA, ethanol has replaced formalin as the primary preservative for animal specimens. However, short-term formalin fixation of specimens might be applied during field collection. In an increasing number of studies, DNA extraction and sequencing have been successfully conducted from formalin-fixed specimens. Here the DNA from five specimens of Triplophysa dalaica (Kessler, 1876) were extracted and performed high-throughput sequencing. Four of the specimens underwent short-term fixation with formalin and were subsequently transferred to ethanol. One was continuously stored in ethanol. No significant difference of DNA quality and amount were observed among these samples. Followed by assembly and annotation, five mitochondrial genomes ranging in length from 16,569 to 16,572 bp were obtained. Additionally, previously published data of other individuals or species were included to perform phylogenetic analyses. In the reconstructed trees, all eight individuals of T. dalaica form a monophyletic group within the Triplophysa branch. The group is divided into three clades: (1) samples from the Yellow River, (2) those from the Yangtze River, and (3) those from the Haihe River, and the Lake Dali Nur. This study sheds initial light on the phylogeographic relationships among different populations of T. dalaica, and will support the research about its evolutionary history in the future.

Abstract Perovskite oxides and organic–inorganic halide perovskite materials, with numerous fascinating features, have been subjected to extensive studies. Most of the properties of perovskite materials are dependence on their ferroelectricity that denoted by remanent polarization (Pr). Thus, the increase of Pr in perovskite films is mainly an effort in material physics. At present, commonplace improvement schemes, i.e., controlling material crystallinity, and post‐annealing by using a high‐temperature process, are normally used. However, a simpler and temporal strategy for Pr improvement is always unavailable to perovskite material researchers. In this study, an organic coating layer, low‐temperature, and vacuum‐free strategy is proposed to improve the Pr, directly increasing the Pr from 36 to 56 µC cm−2. Further study finds that the increased Pr originates from the suppression of the oxygen defects and Ti defects. This organic coating layer strategy for passivating the defects may open a new way for the preparation of higher‐performance and cost‐effective perovskite products, further improving its prospective for application in the electron devices field.

Abstract Pseudomonas putida has become an increasingly important chassis for producing valuable bioproducts. This development is not least due to the ever‐improving genetic toolbox, including gene and genome editing techniques. Here, we present a novel, one‐plasmid design of a critical genetic tool, the pEMG/pSW system, guaranteeing one engineering cycle to be finalized in 3 days. The pEMG/pSW system proved in the last decade to be valuable for targeted genome engineering in Pseudomonas, as it enables the deletion of large regions of the genome, the integration of heterologous gene clusters or the targeted generation of point mutations. Here, to expedite genetic engineering, two alternative plasmids were constructed: (1) The sacB gene from Bacillus subtilis was integrated into the I‐SceI expressing plasmid pSW‐2 as a counterselection marker to accelerated plasmid curing; (2) double‐strand break introducing gene I‐sceI and sacB counterselection marker were integrated into the backbone of the original pEMG vector, named pEMG‐RIS. The single plasmid of pEMG‐RIS allows rapid genome editing despite the low transcriptional activity of a single copy of the I‐SceI encoding gene. Here, the usability of the pEMG‐RIS is shown in P. putida KT2440 by integrating an expression cassette including an msfGFP gene in 3 days. In addition, a large fragment of 12.1 kb was also integrated. In summary, we present an updated pEMG/pSW genome editing system that allows efficient and rapid genome editing in P. putida. All plasmids designed in this study will be available via the Addgene platform.

Abstract The peptidyl-prolyl cis-trans isomerase Pin1 is a pivotal therapeutic target in cancers, but the regulation of Pin1 protein stability is largely unknown. High Pin1 expression is associated with SUMO1-modified protein hypersumoylation in glioma stem cells (GSCs), but the underlying mechanisms remain elusive. Here we demonstrate that Pin1 is deubiquitinated and stabilized by USP34, which promotes isomerization of the sole SUMO E2 enzyme Ubc9, leading to SUMO1-modified hypersumoylation to support GSC maintenance. Pin1 interacts with USP34, a deubiquitinase with preferential expression and oncogenic function in GSCs. Such interaction is facilitated by Plk1-mediated phosphorylation of Pin1. Disruption of USP34 or inhibition of Plk1 promotes poly-ubiquitination and degradation of Pin1. Furthermore, Pin1 isomerizes Ubc9 to upregulate Ubc9 thioester formation with SUMO1, which requires CDK1-mediated phosphorylation of Ubc9. Combined inhibition of Pin1 and CDK1 with sulfopin and RO3306 most effectively suppresses orthotopic tumor growth. Our findings provide multiple molecular targets to induce Pin1 degradation and suppress hypersumoylation for cancer treatment.

There is a close relationship between potassium (K) and nitrogen (N). However, the roles of K under high N conditions remain unclear. Using a hydroponics approach, we monitored the morphological, physiological, and molecular changes in M9T337 apple (Malus domestica) rootstocks under different nitrate (10 and 30 mmol·L−1 NO3−) and K supply (0.5, 6, 10, and 20 mmol·L−1 K+) conditions. Results revealed that high nitrate inhibited the root growth of M9T337 rootstocks, downregulated the expressions of K transporter genes (MdPT5, MdHKT1, and MdATK1), and reduced the net NO3− and K+ influx at the surface of roots, thereby resulting in an N/K imbalance in rootstocks. Further investigation showed that 10 mmol·L−1 K increased the activity of N metabolic enzymes (NR, GS, NiR, and GOGAT), upregulated the expressions of genes related to nitrate uptake and transport (MdNRT1.1, MdNRT1.2, MdNRT1.5, and MdNRT2.4), promoted 15N transport from the roots to the shoots, optimized leaf N distribution, and improved photosynthetic N utilization efficiency under high nitrate conditions. These results suggest that the negative effects of high nitrate may be related to the N/K imbalance and that reducing N/K in plants by increasing K supply level can effectively alleviate the inhibition of N assimilation by high nitrate stress.

Abstract Copper-based catalysts serve as the predominant methanol steam reforming material although several fundamental issues remain ambiguous such as the identity of active center and the aspects of reaction mechanism. Herein, we prepare Cu/Cu(Al)O x catalysts with amorphous alumina-stabilized Cu2O adjoining Cu nanoparticle to provide Cu0−Cu+ sites. The optimized catalyst exhibits 99.5% CH3OH conversion with a corresponding H2 production rate of 110.8 μmol s−1 gcat −1 with stability over 300 h at 240 °C. A binary function correlation between the CH3OH reaction rate and surface concentrations of Cu0 and Cu+ is established based on kinetic studies. Intrinsic active sites in the catalyst are investigated with in situ spectroscopy characterization and theoretical calculations. Namely, we find that important oxygen-containing intermediates (CH3O* and HCOO*) adsorb at Cu0−Cu+ sites with a moderate adsorption strength, which promotes electron transfer from the catalyst to surface species and significantly reduces the reaction barrier of the C−H bond cleavage in CH3O* and HCOO* intermediates.

Insulin secretion by pancreatic islets plays a vital role in regulating blood glucose levels. Nevertheless, the mechanism responsible for this dynamic insulin secretion has not been completely understood, particularly at the single islet level. In this study, we have successfully developed an easy microfluidic platform that allows for the exploration of dynamic glucose-stimulated insulin secretion (GSIS) at the single islet level. With the utilization of this platform, we evaluated dynamic GSIS from single islets isolated from both normal and diabetic rats. Our results demonstrate that islets can be categorized into three types based on their dynamic GSIS: Type I exhibits a biphasic GSIS profile with a fast first phase and flat second phase; Type II also has a biphasic GSIS profile with a fast first phase but a slow increased second phase; Type III displays only a slowly increased second phase and lacks a fast first phase. RNA sequencing analysis demonstrated that the cell type and exocytosis-specific genes are consistent with the proportion of cells and insulin release kinetics among the three types of islets, respectively. Moreover, our findings suggest that high expression of Atp5pb is anti-correlated with the first phase of insulin secretion. Furthermore, we revealed that diabetic islets exhibit only the type I GSIS response, indicating a deliberate impairment of the second phase of insulin secretion. Together, this device serves as a crucial tool in the research field of islets and diabetes, allowing researchers to investigate islet functional heterogeneity and identity at the single islet level.

The understanding of the link between the gut-bone axis is growing yearly, but the mechanisms involved are not yet clear. Our study analyzed the role of Sestrin2 (SESN2)pathway in the gut-bone axis. We established an osteoarthritis (OA) model in Sprague-Dawley (SD) rats using the anterior cruciate ligament transection (ACLT) procedure, followed by a dietary intervention with varying levels of dietary fiber content for 8 weeks. By 16S rRNA sequencing of the gut microbiota, we found that high dietary fiber (HDF) intake could significantly increase the Bacillota-dominant gut microbiota. Meanwhile, enzyme linked immunosorbent assay (ELISA) and histological analysis showed that intervention with HDF could reduce the degree of bone and joint lesions and inflammation. We hypothesize that HDF increased the dominant flora of Bacillota, up-regulated the expression of SESN2 in knee joint, and reduced gut permeability, thereby reducing systemic inflammatory response and the degree of bone and joint lesions. Therefore, the present study confirms that changes in gut microbiota induced by increased dietary fiber intake delayed the onset of OA by promoting up-regulation of SESN2 expression at the knee joint to maintain chondrocyte activity and reduce synovial inflammation.

This article explores the technology of recognizing non-cooperative communication behavior, with a specific emphasis on analyzing communication station signals. Conventional techniques for analyzing signal data frames to determine their identity, while precise, do not have the ability to operate in real-time. In order to tackle this issue, we developed a pragmatic architecture for recognizing communication behavior and a system based on polling. The method utilizes a one-dimensional convolutional neural network (CNN) to segment data, hence improving its ability to recognize various communication activities. The study assesses the reliability of CNN in several real-world scenarios, examining its accuracy in the presence of noise interference, varying lengths of interception signals, interferences at different frequency points, and dynamic changes in outpost locations. The experimental results confirm the efficacy and dependability of the convolutional neural network in recognizing communication behavior in various contexts.

Abstract Background Asthma is widely recognized as an inflammatory disorder. In the context of this inflammatory microenvironment, the involvement of hypoxia and its impact on related pathways have drawn considerable attention. However, the exact role of hypoxia, a prevalent environmental factor, in the development and progression of asthma remains poorly understood. Methods Mice were treated with house dust mite (HDM) extracts for 23 days to induce asthma. Mice were divided into room air (RA) group and intermittent hypoxic (IH) group by exposing to different conditions and IH preconditioning (IHP) were underwent to the above groups before the hypoxic regimen. Airway inflammation in mice was evaluated by airway hyperresponsiveness, excessive mucus secretion, and recruitment of inflammatory cells. Immunohistochemistry was employed to quantify the expression levels of NF‐κB. Subsequently, the dose of allergen was modified to investigate whether the impact of hypoxia on asthma is affected by different doses of allergens. Result Compared to the RA and IH groups, HDM‐treated mice in the IHP group exhibited aggravated inflammatory cell infiltration and airway hyperresponsiveness (p＜.05). Moreover, there was an increased release of inflammatory mediators and higher expression levels of NF‐κB (p＜.05). Importantly, the impact ia on asthma was found to be influenced by high dose of allergen (p＜.05). Conclusion IHP treatment potentially exacerbates HDM‐induced airway inflammation in asthma, with the involvement of NF‐κB, particularly under high‐dose allergen stimulation.

IntroductionLung adenocarcinoma (LUAD) is a prevalent form of lung cancer originating from lung glandular cells with low survival rates despite recent therapeutic advances due to its diverse and complex nature. Recent evidence suggests a link between ferroptosis and the effectiveness of anti-PD-L1 therapy, with potential synergistic effects.MethodsOur study comprehensively analyzed the expression patterns of ferroptosis regulators in LUAD and their association with prognosis and PD-L1 expression. Furthermore, we identified two distinct subtypes of LUAD through consensus clustering of ferroptosis regulators, revealing significant tumor heterogeneity, divergent PD-L1 expression, and varying prognoses between the subtypes.ResultsAmong the selected ferroptosis regulators, SLC7A11 emerged as an independent prognostic marker for LUAD patients and exhibited a negative correlation with PD-L1 expression. Subsequent investigations revealed high expression of SLC7A11 in the LUAD population. In vitro experiments demonstrated that overexpression of SLC7A11 led to reduced PD-L1 expression and inhibited ferroptosis in A549 cells, underscoring the significant role of SLC7A11 in LUAD. Additionally, pan-cancer analyses indicated an association between SLC7A11 and the expression of immune checkpoint genes across multiple cancer types with poor prognoses.DiscussionFrom a clinical standpoint, these findings offer a foundation for identifying and optimizing potential combination strategies to enhance the therapeutic effectiveness of immune checkpoint inhibitors and improve the prognosis of patients with LUAD.

Solving arithmetic word problems ($ AWPs $) that involve deep implicit relations can be quite challenging. However, the paper proposed two approaches to tackle this issue. The first approach used the modifier-to-matrix ($ MTM $) model to extract noun modification components from the problem text. Specifically, a missing entity recovery ($ MER $) model translated explicit expressions into a node dependency graph ($ NDG $). The nodes on the graph then recursively acquired connections from the knowledge base through the $ MER $ model until the goal was achieved with a known quantity. The solving engine then selected the appropriate knowledge as the prompt. The second approach proposed in the paper was a comprehensive one that combined explicit and implicit knowledge to enhance reasoning abilities. The experimental results of the dataset demonstrate that the proposed algorithm is superior to the baseline algorithms in solving $ AWPs $ that require deep implicit relations.

Abstract Acute myocardial infarction (AMI) is a life-threatening disease when sudden blockage of coronary artery occurs. As the most specific biomarker, cardiac troponin I (cTnI) is usually checked separately to diagnose or eliminate AMI, and achieving the accurate detection of cTnI is of great significance to patients’ life and health. Compared with other methods, fluorescent detection has the advantages of simple operation, high sensitivity and wide applicability. However, due to the strong fluorescence interference of biological molecules in body fluids, it is often difficult to obtain high sensitivity. In order to solve this problem, in this study, surface acoustic wave separation is designed to purify the target to achieve more sensitive detection performance of fluorescent detection. Specifically, the interference of background noise is almost completely removed on a microfluidic chip by isolating microbeads through acoustic radiation force, on which the biomarkers are captured by the immobilized detection probe. And then, the concentration of cTnI in human serum is detected by the fluorescence intensity change of the isolated functionalized beads. By this way, the detection limit of our biosensor calculated by 3σ/K method is 44 pg/mL and 0.34 ng/mL in PBS buffer and human serum respectively. Finally, the reliability of this method has been validated by comparison with clinical tests from the nephelometric analyzer in hospital.

The key to promoting intelligent construction is to integrate the digital twin technology form the operation mode of virtual and real integration. And the industrial metaverse based on digital twin is the future development direction of intelligent mining face. The concept of virtual and real integration operation mode of fully-mechanized mining face based on virtual reality-digital twin-cyber physical system-industrial metaverse is proposed. It has six connotation characteristics, such as display and off-line simulation, monitoring and auxiliary operation, online simulation and preview. It is an evolution process from low-level display simulation to high-level deep integration function. Finally, it have four abilities : the ability of reproduction mapping from real to virtual precision, the ability of reasoning and forecasting decision-making in virtual iteration, the ability of reproduction control from virtual to real, the ability of seamless cooperation between virtual and real human-computer, and the ability of lean management. The four capabilities of industrial metaverse and the key technologies to realize industrial metaverse are analyzed.Based on the existing monitoring, decision-making and control capabilities, AR remote assistance technology that can strengthen the cooperation ability between field operators and remote operators, robot cooperation technology that can strengthen the safety of operators, and virtual human technology that can use AI-driven operation in virtual space are integrated to build a hydraulic support adjusting experimental system based on industrial metaveise,and preliminary understanding of the application of industrial metaverse in coal mining.

The effect of H2 activation on the performance of CuFeOx catalyst for low-temperature CO oxidation was investigated. The characterizations of XRD, XPS, H2-TPR, O2-TPD, and in situ DRIFTS were employed to establish the relationship between physicochemical property and catalytic activity. The results showed that the CuFeOx catalyst activated with H2 at 100 °C displayed higher performance, which achieved 99.6% CO conversion at 175 °C. In addition, the H2 activation promoted the generation of Fe2+ species, and more oxygen vacancy could be formation with higher concentration of Oα species, which improved the migration rate of oxygen species in the reaction process. Furthermore, the reducibility of the catalyst was enhanced significantly, which increased the low-temperature activity. Moreover, the in situ DRIFTS experiments revealed that the reaction pathway of CO oxidation followed MvK mechanism at low temperature (+-CO and carbonate species were the main reactive intermediates, and the H2 activation increased the concentration of Cu+ species and accelerated the decomposition carbonate species, thus improving the catalytic performance effectively.