Baicalein suppresses Coxsackievirus B3 replication by inhibiting caspase-1 and viral protease 2A.

Myocarditis is an inflammatory disease of the cardiac muscle and one of the primary causes of dilated cardiomyopathy. Group B coxsackievirus (CVB) is one of the leading causative pathogens of viral myocarditis, which primarily affects children and young adults. Due to the lack of vaccines, the development of antiviral medicines is crucial to controlling CVB infection and the progression of myocarditis. In this study, we investigated the antiviral effect of baicalein, a flavonoid extracted from Scutellaria baicaleinsis. Our results demonstrated that baicalein treatment significantly reduced cytopathic effect and increased cell viability in CVB3-infected cells. In addition, significant reductions in viral protein 3D, viral RNA, and viral particles were observed in CVB3-infected cells treated with baicalein. We found that baicalein exerted its inhibitory effect in the early stages of CVB3 infection. Baicalein also suppressed viral replication in the myocardium and effectively alleviated myocarditis induced by CVB3 infection. Our study revealed that baicalein exerts its antiviral effect by inhibiting the activity of caspase-1 and viral protease 2A. Taken together, our findings demonstrate that baicalein has antiviral activity against CVB3 infection and may serve as a potential therapeutic option for the myocarditis caused by enterovirus infection.

Accelerated and precise identification of antioxidants and pesticides using a smartphone-based colorimetric sensor array.

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1-10 µM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.

Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery.

Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network's feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm's detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network's feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network's feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address issues such as excessive optimization of negative samples and poor gradient-descent direction, thereby strengthening the network's effective recognition of positive samples. Experimental results show that the improved YOLOX network has better detection performance, with mAP@50 and mAP@50_95 increasing by 6.4% and 2.17%, respectively, compared to the traditional YOLOX network. In multi-target flame and small-target flame scenarios, the improved YOLO model achieved a mAP of 96.3%, outperforming deep learning algorithms such as FasterRCNN, SSD, and YOLOv5 by 33.5%, 7.7%, and 7%, respectively. It has a lower omission rate and higher detection accuracy, and it is capable of handling small-target detection tasks in complex fire environments. This can provide support for UAV patrol and rescue applications from a high-altitude perspective.

Strong Second-Harmonic Generation Induced by a Triphenylamine-Based Bismuth-Organic Framework for Photocatalytic Activity Enhancement.

Taking advantage of the well-defined geometry of metal centers and highly directional metal-ligand coordination bonds, metal-organic frameworks (MOFs) have emerged as promising candidates for nonlinear optical (NLO) materials. In this work, taking a photoresponsive carboxylate triphenylamine derivative as an organic ligand, a bismuth-based MOF, Bi-NBC, NBC = 4',4‴,4‴″-nitrilotris(([1,1'-biphenyl]-4-carboxylic acid)) is obtained. Structure determination reveals that it is a potential NLO material derived from its noncentrosymmetric structure, which is finally confirmed by its rarely strong second harmonic generation (SHG) effect. Theoretical calculations reveal that the potential difference around Bi atoms is large; therefore, it leads to a strong local built-in electric field, which greatly facilitates the charge separation and transfer and finally improves the photocatalytic performance. Our results provide a reference for the exploration of MOFs with NLO properties.

"All in One" Strategy for Achieving Superprotonic Conductivity by Incorporating Strong Acids into a Robust Imidazole-Linked Covalent Organic Framework.

The fabrication of solid-state proton-conducting electrolytes possessing both high performance and long-life reusability is significant but challenging. An "all-in-one" composite, H3PO4@PyTFB-1-SO3H, including imidazole, sulfonic acid, and phosphoric acid, which are essential for proton conduction, was successfully prepared by chemical post-modification and physical loading in the rationally pre-synthesized imidazole-based nanoporous covalent organic framework (COF), PyTFB-1. The resultant H3PO4@PyTFB-1-SO3H exhibits superhigh proton conductivity with its value even highly up to 1.15 × 10-1 S cm-1 at 353 K and 98% relative humidity (RH), making it one of the highest COF-based composites reported so far under the same conditions. Experimental studies and theoretical calculations further confirmed that the imidazole and sulfonic acid groups have strong interactions with the H3PO4 molecules and the synergistic effect of these three groups dramatically improves the proton conductivity properties of H3PO4@PyTFB-1-SO3H. This work demonstrated that by aggregating multiple proton carriers into one composite, effective proton-conducting electrolyte can be feasibly achieved.

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology.

The quantum denoising technology efficiently removes noise from images; however, the existing algorithms are only effective for additive noise and cannot remove multiplicative noise, such as speckle noise in synthetic aperture radar (SAR) images. In this paper, based on the grayscale morphology method, a quantum SAR image denoising algorithm is proposed, which performs morphological operations on all pixels simultaneously to remove the noise in the SAR image. In addition, we design a feasible quantum adder to perform cyclic shift operations. Then, quantum circuits for dilation and erosion are designed, and the complete quantum circuit is then constructed. For a 2n×2n quantum SAR image with q grayscale levels, the complexity of our algorithm is O (n+q). Compared with classical algorithms, it achieves exponential improvement and also has polynomial-level improvements than existing quantum algorithms. Finally, the feasibility of our algorithm is validated on IBM Q.

Robust links in photoactive covalent organic frameworks enable effective photocatalytic reactions under harsh conditions.

Developing heterogeneous photocatalysts for the applications in harsh conditions is of high importance but challenging. Herein, by converting the imine linkages into quinoline groups of triphenylamine incorporated covalent organic frameworks (COFs), two photosensitive COFs, namely TFPA-TAPT-COF-Q and TFPA-TPB-COF-Q, are successfully constructed. The obtained quinoline-linked COFs display improved stability and photocatalytic activity, making them suitable photocatalysts for photocatalytic reactions under harsh conditions, as verified by the recyclable photocatalytic reactions of organic acid involving oxidative decarboxylation and organic base involving benzylamine coupling. Under strong oxidative condition, the quinoline-linked COFs show a high efficiency up to 11831.6 µmol·g-1·h-1 and a long-term recyclable usability for photocatalytic production of H2O2, while the pristine imine-linked COFs are less catalytically active and easily decomposed in these harsh conditions. The results demonstrate that enhancing the linkage robustness of photoactive COFs is a promising strategy to construct heterogeneous catalysts for photocatalytic reactions under harsh conditions.

Natural Products-Pyrazine Hybrids: A Review of Developments in Medicinal Chemistry.

Pyrazine is a six-membered heterocyclic ring containing nitrogen, and many of its derivatives are biologically active compounds. References have been downloaded through Web of Science, PubMed, Science Direct, and SciFinder Scholar. The structure, biological activity, and mechanism of natural product derivatives containing pyrazine fragments reported from 2000 to September 2023 were reviewed. Publications reporting only the chemistry of pyrazine derivatives are beyond the scope of this review and have not been included. The results of research work show that pyrazine-modified natural product derivatives have a wide range of biological activities, including anti-inflammatory, anticancer, antibacterial, antiparasitic, and antioxidant activities. Many of these derivatives exhibit stronger pharmacodynamic activity and less toxicity than their parent compounds. This review has a certain reference value for the development of heterocyclic compounds, especially pyrazine natural product derivatives.

[Characteristics and Ecological Risk Assessment of Pharmaceutical Active Compounds in Guangdong Province].

Pharmaceutically active compounds(PhACs) have become a class of new pollutants in the environment after extensive production and use of PhACs in China. To investigate the pollution characteristics of PhACs in Guangdong Province, raw sewage was collected from 186 sewage treatment plants in 21 cities, including 178 townships and administrative districts in Guangdong Province. The pollution levels of ten typical PhACs in influent water of sewage treatment plants were analyzed using automatic solid phase extraction and high performance liquid chromatography-triple quadrupole mass spectrometry. The spatial distribution characteristics of PhACs in Guangdong Province were fully revealed, and the potential ecological risks of PhACs were evaluated. The results showed that PhACs were detected in all wastewater plants, and the mass concentration of PhACs ranged from 21.00 to 9558.25 ng·L-1. Metoprolo, acetaminophen, bezafibrate, and caffeine were the main pollutants. In terms of spatial distribution, the average mass concentration of ΣPhACs in various regions of Guangdong Province was in the following order:Pearl River Delta>North Guangdong>East Guangdong≈West Guangdong. When the mass concentration of ΣPhACs was over 2500 ng·L-1 in the influent water of sewage treatment plants, the concentration of PhACs in effluent was estimated according to the sewage disposal technology. The ecological risk of PhACs was carried out based on the effluent. The results revealed that the ecological risk of PhACs was low in Guangdong Province, and the risk of bezafibrate was moderate in the cities of Shaoguan, Jiangmen, and Shenzhen. The highest ecological risk of ΣPhACs was located in Shaoguan.

Application of Quinoline Ring in Structural Modification of Natural Products.

Natural compounds are rich in pharmacological properties that are a hot topic in pharmaceutical research. The quinoline ring plays important roles in many biological processes in heterocycles. Many pharmacological compounds, including saquinavir and chloroquine, have been marketed as quinoline molecules with good anti-viral and anti-parasitic properties. Therefore, in this review, we summarize the medicinal chemistry of quinoline-modified natural product quinoline derivatives that were developed by several research teams in the past 10 years and find that these compounds have inhibitory effects on bacteria, viruses, parasites, inflammation, cancer, Alzheimer's disease, and others.

Highly Enhancing CO2 Photoreduction by Metallization of an Imidazole-linked Robust Covalent Organic Framework.

Converting CO2 into value-added chemicals to solve the issues caused by carbon emission is promising but challenging. Herein, by embedding metal ions (Co2+ , Ni2+ , Cu2+ , and Zn2+ ) into an imidazole-linked robust photosensitive covalent organic framework (PyPor-COF), effective photocatalysts for CO2 conversion are rationally designed and constructed. Characterizations display that all of the metallized PyPor-COFs (M-PyPor-COFs) display remarkably high enhancement in their photochemical properties. Photocatalysis reactions reveal that the Co-metallized PyPor-COF (Co-PyPor-COF) achieves a CO production rate as high as up to 9645 µmol g-1 h-1 with a selectivity of 96.7% under light irradiation, which is more than 45 times higher than that of the metal-free PyPor-COF, while Ni-metallized PyPor-COF (Ni-PyPor-COF) can further tandem catalyze the generated CO to CH4 with a production rate of 463.2 µmol g-1 h-1 . Experimental analyses and theory calculations reveal that their remarkable performance enhancement on CO2 photoreduction should be attributed to the incorporated metal sites in the COF skeleton, which promotes the adsorption and activation of CO2 and the desorption of generated CO and even reduces the reaction energy barrier for the formation of different intermediates. This work demonstrates that by metallizing photoactive COFs, effective photocatalysts for CO2 conversion can be achieved.

Anisomycin inhibits Coxsackievirus B replication by promoting the lysosomal degradation of eEF1A1.

Group B Coxsackieviruses (CVB) are non-enveloped small RNA viruses in the genus Enterovirus, family Picornaviridae. CVB infection causes diverse conditions from common cold to myocarditis, encephalitis, and pancreatitis. No specific antiviral is available for the treatment of CVB infection. Anisomycin, a pyrrolidine-containing antibiotic and translation inhibitor, was reported to inhibit the replication of some picornaviruses. However, it is unknown if anisomycin can act as an antiviral against CVB infection. Here we observed that anisomycin showed potent inhibition on CVB type 3 (CVB3) infection with negligible cytotoxicity when applied at the early stage of virus infection. Mice infected with CVB3 showed markedly alleviated myocarditis with reduced viral replication. We found that CVB3 infection significantly increased the transcription of eukaryotic translation elongation factor 1 alpha 1 (eEF1A1). CVB3 replication was suppressed by EEF1A1 knockdown, while elevated by EEF1A1 overexpression. Similar to the effect of CVB3 infection, EEF1A1 transcription was increased in response to anisomycin treatment. However, eEF1A1 protein level was decreased with anisomycin treatment in a dose-dependent manner in CVB3-infected cells. Moreover, anisomycin promoted eEF1A1 degradation, which was inhibited by the treatment of chloroquine but not MG132. We demonstrated that eEF1A1 interacted with the heat shock cognate protein 70 (HSP70), and eEF1A1 degradation was inhibited by LAMP2A knockdown, implicating that eEF1A1 is degraded through chaperone-mediated autophagy. Taken together, we demonstrated that anisomycin, which inhibits CVB replication through promoting the lysosomal degradation of eEF1A1, could be a potential antiviral candidate for the treatment of CVB infection.

Highly Effective Generation of Singlet Oxygen by an Imidazole-Linked Robust Photosensitizing Covalent Organic Framework.

Developing effective photosensitizers to initiate the generation of singlet oxygen (1O2) is of great significance in both chemistry and physiology. Herein, linking the photoactive porphyrin moieties by in situ-formed robust imidazole groups, a covalent organic framework (COF), PyPor-COF, was successfully designed and synthesized. Detailed characterizations reveal that it not only possesses high crystallinity, permanent porosity, and robust stability but also shows a semiconductive photoresponse activity. As demonstrated by electron paramagnetic resonance experiments, the COF can initiate the generation of 1O2 efficiently under visible-light irradiation, the efficiency of which is higher than that of the pristine porphyrin-based reactant and even higher than some commonly used commercially available photosensitizing agents. Anticancer experiments prove that it can efficiently trigger the production of 1O2 in a physiological environment. This work demonstrates that the imidazole-linked porphyrin-incorporated COF is a highly promising photosensitizer that can even be applied in photodynamic therapy.

A CRISPR/Cas12a-assisted rapid detection platform by biosensing the apxIVA of Actinobacillus pleuropneumoniae.

Actinobacillus pleuropneumoniae is an important respiratory pig pathogen that causes substantial losses in the worldwide swine industry. Chronic or subclinical infection with no apparent clinical symptoms poses a challenge for preventing transmission between herds. Rapid diagnostics is important for the control of epidemic diseases. In this study, we formulated an A. pleuropneumoniae species-specific apxIVA-based CRISPR/Cas12a-assisted rapid detection platform (Card) that combines recombinase polymerase amplification (RPA) of target DNA and subsequent Cas12a ssDNase activation. Card has a detection limit of 10 CFUs of A. pleuropneumoniae, and there is no cross-reactivity with other common swine pathogens. The detection process can be completed in 1 h, and there was 100% agreement between the conventional apxIVA-based PCR and Card in detecting A. pleuropneumoniae in lung samples. Microplate fluorescence readout enables high-throughput use in diagnostic laboratories, and naked eye and lateral flow test readouts enable use at the point of care. We conclude that Card is a versatile, rapid, accurate molecular diagnostic platform suitable for use in both laboratory and low-resource settings.

Highly Effective Proton-Conductive Matrix-Mixed Membrane Based on a -SO3H-Functionalized Polyphosphazene.

A polyphosphazene with in-built -SO3H moieties (PP-PhSO3H) was facilely synthesized by the polymeric combination of hexachlorocyclotriphosphazene (HCCP) and sulfonate p-phenylenediamine. Characterization reveals that it is a highly stable amorphous polymer. Proton conductivity investigations showed that the synthesized PP-PhSO3H exhibits a proton conductivity of up to 6.64 × 10-2 S cm-1 at 353 K and 98% relative humidity (RH). This value is almost 2 orders of magnitude higher than the corresponding value for its -SO3H-free analogue, PP-Ph, which is 1.72 × 10-4 S cm-1 when measured under the same condition. Consequently, matrix-mixed membranes (labeled PP-PhSO3H-PAN) were further prepared by mixing PP-PhSO3H with polyacrylonitrile (PAN) in different ratios to test its potential application in the proton-exchange membrane (PEM) fuel cell. The analysis results indicate that when the weight ratio of PP-PhSO3H/PAN is 3:1 [named PP-PhSO3H-PAN (3:1)], its proton conductivity can reach up to 5.05 × 10-2 S cm-1 at 353 K and 98% RH, which is even comparable with those of proton-conductive electrolytes currently used in PEM fuel cells. Furthermore, the continuous test demonstrates that the PP-PhSO3H-PAN (3:1) has long-life reusability. This research reveals that by using phosphazene and sulfonated multiple-amine modules as precursors, organic polymers with excellent proton conductivity for the assembly of matrix-mixed membranes in PEM fuel cells can be easily synthesized by a simple polymeric process.

Highly Effective Proton-Conduction Matrix-Mixed Membrane Derived from an -SO3H Functionalized Polyamide.

Developing a low-cost and effective proton-conductive electrolyte to meet the requirements of the large-scale manufacturing of proton exchange membrane (PEM) fuel cells is of great significance in progressing towards the upcoming "hydrogen economy" society. Herein, utilizing the one-pot acylation polymeric combination of acyl chloride and amine precursors, a polyamide with in-built -SO3H moieties (PA-PhSO3H) was facilely synthesized. Characterization shows that it possesses a porous feature and a high stability at the practical operating conditions of PEM fuel cells. Investigations of electrochemical impedance spectroscopy (EIS) measurements revealed that the fabricated PA-PhSO3H displays a proton conductivity of up to 8.85 × 10-2 S·cm-1 at 353 K under 98% relative humidity (RH), which is more than two orders of magnitude higher than that of its -SO3H-free analogue, PA-Ph (6.30 × 10-4 S·cm-1), under the same conditions. Therefore, matrix-mixed membranes were fabricated by mixing with polyacrylonitrile (PAN) in different ratios, and the EIS analyses revealed that its proton conductivity can reach up to 4.90 × 10-2 S·cm-1 at 353 K and a 98% relative humidity (RH) when the weight ratio of PA-PhSO3H:PAN is 3:1 (labeled as PA-PhSO3H-PAN (3:1)), the value of which is even comparable with those of commercial-available electrolytes being used in PEM fuel cells. Additionally, continuous tests showed that PA-PhSO3H-PAN (3:1) possesses a long-life reusability. This work demonstrates, using the simple acylation reaction with the sulfonated module as precursor, that low-cost and highly effective proton-conductive electrolytes for PEM fuel cells can be facilely achieved.

Facile construction of a highly proton-conductive matrix-mixed membrane based on a -SO3H functionalized polyamide.

Developing a facile strategy to construct low-cost and efficient proton-conductive electrolytes is pivotal in the practical application of proton exchange membrane (PEM) fuel cells. Herein, a polyamide with in-built -SO3H moieties, PA(PhSO3H)2, was synthesized via a simple one-pot polymeric acylation process. Investigations via electrochemical impedance spectroscopy (EIS) measurements revealed that the fabricated PA(PhSO3H)2 displays a proton conductivity of up to 5.54 × 10-2 S cm-1 at 353 K under 98% relative humidity (RH), which is more than 2 orders of magnitude higher than that of its -SO3H-free analogue PA(Ph)2 (2.38 × 10-4 S cm-1) under the same conditions. Therefore, after mixing with polyacrylonitrile (PAN) at different ratios, PA(PhSO3H)2-based matrix-mixed membranes were subsequently made and the analysis results revealed that the proton conductivity can reach up to 5.82 × 10-2 S cm-1 at 353 K and 98% RH when the weight ratio of PA(PhSO3H)2 : PAN is in 3 : 1 (labeled as PA(PhSO3H)2-PAN(3 : 1)), the value of which is comparable even to those of commercially available electrolytes that are used in PEM fuel cells. In addition, continuous testing shows that PA(PhSO3H)2-PAN(3 : 1) possesses long-life reusability. This work demonstrates that, utilizing the simple reaction of polymeric acylation with a sulfonated module as a precursor, highly effective proton-conductive membranes for PEM fuel cells can be achieved in a facile manner.

Design, synthesis and biological evaluation of 4-amino-quinolines as antitumor agents.

Fifteen novel 4-amino-quinolines (I1-III3) as antitumor agent were synthesized by p-nitroaniline and ethoxymethylene malonic ester (EMME) via condensation, cyclization, hydrolysis, decarboxylation, chlorination, nucleophilic substitution, reduction and amidation. The antitumor activity of compounds I1-III3 was evaluated on SGC-7901, BEL-7402 and A549 cancer cell lines. In vitro bioassay indicated that some compounds showed different degree activity against all tested cancer cell lines. Compound I1, I4 and II2 exhibited high effects against A549 cell lines (IC50 = 1.34µM, 1.36µM and 3.00µM, respectively). In addition, the result of molecular docking showed that compound I1, I4 and II2 could dock into the pocket of EGFR.

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning.

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN.