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Abstract Zika virus (ZIKV) infection may lead to severe neurological consequences, including seizures, and early infancy death. However, the involved mechanisms are still largely unknown. TRPC channels play an important role in regulating nervous system excitability and are implicated in seizure development. We investigated whether TRPCs might be involved in the pathogenesis of ZIKV infection. We found that ZIKV infection increases TRPC4 expression in host cells via the interaction between the ZIKV-NS3 protein and CaMKII, enhancing TRPC4-mediated calcium influx. Pharmacological inhibition of CaMKII decreased both pCREB and TRPC4 protein levels, whereas the suppression of either TRPC4 or CaMKII improved the survival rate of ZIKV-infected cells and reduced viral protein production, likely by impeding the replication phase of the viral life cycle. TRPC4 or CaMKII inhibitors also reduced seizures and increased the survival of ZIKV-infected neonatal mice and blocked the spread of ZIKV in brain organoids derived from human-induced pluripotent stem cells. These findings suggest that targeting CaMKII or TRPC4 may offer a promising approach for developing novel anti-ZIKV therapies, capable of preventing ZIKV-associated seizures and death.

Abstract Human enterovirus A71 (EV‐A71) is a significant etiological agent responsible for epidemics of hand, foot, and mouth disease (HFMD) in Asia‐Pacific regions. There are presently no licensed antivirals against EV‐A71, and the druggable target for EV‐A71 remains very limited. The phenotypic hit 10,10′‐bis(trifluoromethyl) marinopyrrole A derivative, herein termed MPA‐CF3, is a novel potent small‐molecule inhibitor against EV‐A71, but its pharmacological target(s) and antiviral mechanisms are not defined. Here, quantitative chemoproteomics deciphered the antiviral target of MAP‐CF3 as host factor coatomer subunit zeta‐1 (COPZ1). Mechanistically, MPA‐CF3 disrupts the interaction of COPZ1 with the EV‐A71 nonstructural protein 2C by destabilizing COPZ1 upon binding. The destruction of this interaction blocks the coatomer‐mediated transport of 2C to endoplasmic reticulum, and ultimately inhibits EV‐A71 replication. Taken together, our study disclosed that MPA‐CF3 can be a structurally novel host‐targeting anti‐EV‐A71 agent, providing a structural basis for developing the COPZ1‐targeting broad‐spectrum antivirals against enteroviruses. The mechanistic elucidation of MPA‐CF3 against EV‐A71 may offer an alternative COPZ1‐involved therapeutic pathway for enterovirus infection.

Abstract N6-methyladenosine (m6A) is present in diverse viral RNA and plays important regulatory roles in virus replication and host antiviral innate immunity. However, the role of m6A in regulating JEV replication has not been investigated. Here, we show that the JEV genome contains m6A modification upon infection of mouse neuroblast cells (neuro2a). JEV infection results in a decrease in the expression of m6A writer METTL3 in mouse brain tissue. METTL3 knockdown by siRNA leads to a substantial decrease in JEV replication and the production of progeny viruses at 48 hpi. Mechanically, JEV triggered a considerable increase in the innate immune response of METTL3 knockdown neuro2a cells compared to the control cells. Our study has revealed the distinctive m6A signatures of both the virus and host in neuro2a cells infected with JEV, illustrating the positive role of m6A modification in JEV infection. Our study further enhances understanding of the role of m6A modification in Flaviviridae viruses.

Severe fever with thrombocytopenia syndrome virus (SFTSV), also known as the Dabie Banda virus, is an emerging tick-borne Bunyavirus that causes severe fever with thrombocytopenia syndrome (SFTS). Currently, symptomatic treatment and antiviral therapy with ribavirin and favipiravir are used in clinical management. However, their therapeutical efficacy is hardly satisfactory in patients with high viral load. In this study, we explored the antiviral effects of selective estrogen receptor modulators (SERMs) on SFTSV infection and the antiviral mechanisms of a representative SERM, bazedoxifene acetate (BZA). Our data show that SERMs potently inhibited SFTSV-induced cytopathic effect (CPE), the proliferation of infectious viral particles, and viral RNA replication and that BZA effectively protected mice from lethal viral challenge. The mode of action analysis reveals that BZA exerts antiviral effects during the post-entry stage of SFTSV infection. The transcriptome analysis reveals that GRASLND and CYP1A1 were upregulated, while TMEM45B and TXNIP were downregulated. Our findings suggest that SERMs have the potential to be used in the treatment of SFTSV infection.

Expressway traffic information is important for guiding driving routes and alleviating traffic congestion. However, the current research on expressway guidance information focuses on existing expressways. In this study, strategies for providing expressway guidance information under reconstruction and expansion scenarios are investigated. Multiple factors of expressway reconstruction and expansion, such as the length of construction areas and the number of lanes occupied by construction areas, are extracted. A panel latent class logit model considering individual heterogeneity is established to fit the survey data collected by 825 respondents. The results show that the proposed panel latent class logit model fits the data best, and the studied drivers could be categorized into three classes, i.e., the information provision time-sensitive class, the information promotion detour class, and the information suppression detour class. The research results can support expressway operators in designing appropriate traffic information provision strategies, providing personalized guidance to drivers, and ensuring the safe operation of expressways in construction areas.

Wellbore instability is always inevitable in shale formation due to hydration, swelling, and dispersion of clay, especially when using water-based drilling fluids (WBDFs). To mitigate the wellbore instability of shale formation and avoid correlative detriments such as formation damage, various nano-silica particles (nano-SiO2) were employed to plug the nano-sized pores, inhibit water invasion into shale, and prevent shale swelling. Firstly, the influence of various nano-SiO2 on rheological and filtration properties of a set WBDF was evaluated. Then, the linear swelling test, shale recovery test, zeta potential test, imbibition test, contact angle measurement, scanning electron microscopy (SEM) observation, and computed tomography (CT) analysis were conducted to assess the characteristic of nano-SiO2. Experimental results showed that solid phase nano-SiO2 could dramatically increase the viscosity and yield point while liquid phase nano-SiO2 only caused small fluctuations on these parameters. Besides, hydrophobic nano-SiO2 displayed better filtration performance than hydrophilic nano-SiO2. On the whole, the hydrophobic nano-SiO2 dispersion, called as nano-2, showed the best performance in WBDFs. Furthermore, nano-2 exhibited better inhibition than hydrophilic nano-SiO2, KCl, and polyether amine (PA). Mechanism analysis demonstrated that nano-2 could improve the hydrophobic degree of shale surface and prevent water from contacting with the shale. Meanwhile, nano-2 plugged the pores and throats in the shale. As a consequence, water in the drilling fluid was prevented from invading the shale, and the shale was inhibited. Nano-2 could form a thin barrier in the surface of shale, and mitigate the damage degree of shale cores after perforation operation. As a result, nano-2 displayed great potential to stabilize shale and protect formation in WBDFs.

A three-dimensional finite element model of a vibratory wheel on soil is established though the use of the ABAQUS software platform to investigate the interaction between the wheel and soil and the resulting dynamic response during vibratory compaction. The extended linear Drucker Prager model is used to reflect the plastic deformation characteristics of the soil. The truncated boundary is treated by using a three-dimensional uniform viscoelastic artificial boundary method. The vibratory responses of the soil under the wheel, including the stress and contact force, are analyzed by using numerical simulations. The results show a decrease in the soil vertical stress at the edge of the vibrating wheel transverse to the wheel path, which may assist in identifying the rolling overlap width of the wheel. Along the wheel path, the vertical stress center is demonstrated to lie ahead of the vibrating wheel mass center, caused by the inclination of the wheel soil contact surface. The contact pressure and total grounding width of the soil under the wheel can be calculated by using the finite element method; only one-third of the total width could produce effective compression deformation.

Abstract Si, as a narrow bandgap semiconductor with a broadband absorption for sunlight, is considered to be a very competitive photoelectrode material for solar‐driven photoelectrochemical (PEC) water splitting. However, there are major barriers in construction of efficient and stable Si‐based PEC cell, including low photovoltage, sluggish reaction kinetics, and poor stability in electrolytes. This review focuses on the strategies to solve these issues and summarizes recent progress. The working principles of PEC water splitting are first introduced. Then the strategies for improving Si‐based photoelectrode performances are discussed, including (1) the regulation of Si surface morphology for enhancing light harvesting, (2) band structure engineering strategies to reduce recombination of photogenerated carriers, and (3) modification of protection layers for long stability and loading cocatalysts on Si‐based photoelectrodes for accelerating water splitting. Lastly, we have presented some issues of Si‐based photoelectrode materials, which should be addressed in future research.

Hantaan virus (HTNV) is the etiological pathogen of hemorrhagic fever with renal syndrome in East Asia. There are currently no effective therapeutics approved for HTNV and other hantavirus infections. We found that griffithsin (GRFT), an algae-derived lectin with broad-spectrum antiviral activity against various enveloped viruses, can inhibit the growth and spread of HTNV. In vitro experiments using recombinant vesicular stomatitis virus (rVSV) with HTNV glycoproteins as a model revealed that the GRFT inhibited the entry of rVSV-HTNV-G into host cells. In addition, we demonstrated that GRFT prevented authentic HTNV infection in vitro by binding to the viral N-glycans. In vivo experiments showed that GRFT partially protected the suckling mice from death induced by intracranial exposure to HTNV. These results demonstrated that GRFT can be a promising agent for inhibiting HTNV infection.

Cationic polymeric materials and cell-penetrating peptides (CPPs) were often used as the delivery vectors in the evaluation of nucleic acid therapeutics. 10-23 DNAzyme is a kind of potential antisense therapeutics by catalytic cleavage of the disease-related RNAs. Here, lipofectamine 2000 and Tat peptide were evaluated for their effect on the catalytic activity of 10-23 DNAzyme, with the observed rate constant, thermal stability, CD spectra, and PAGE analysis, with a duplex DNA mimicking DNAzyme-substrate as a control. It was shown that the cationic carriers had a negative effect on the catalytic performance of the 10-23 DNAzyme. Significantly, the destabilizing effect of the cationic carriers on the duplex formation was noteworthy, as a duplex formation is an essential prerequisite in the silencing mechanisms of antisense and RNAi.

ObjectiveArtemisinin is an organic compound that comes from Artemisia annua. Artemisinin treatment is the most important and effective method for treating malaria. Bibliometric analysis was carried out to identify the global research trends, hot spots, scientific frontiers, and output characteristics of artemisinin from 2000 to 2021.MethodsPublications and their recorded information from 2000 to 2021 were retrieved through the Web of Science Core Collection (WoSCC). Using VOSviewer and Citespace, the hotspots and trends of studies on artemisinin were visualized.ResultsA total of 8,466 publications were retrieved, and for the past 22 years, the annual number of publications associated with artemisinin kept increasing. The United States published most papers. The H-index and number of citations of the United States ranked first. The University of Oxford and MALARIA JOURNAL were the most productive affiliation and journal, respectively. A paper written by E.A. Ashley in 2011 achieved the highest global citation score. Keywords, such as “malaria,” “artesunate,” “plasmodium-falciparum,” “in-vitro,” “artemisinin resistance,” “plasmodium falciparum,” “resistance,” and “artemether-lumefantrine,” appeared most frequently. The research on artemisinin includes clinical research and animal and cell experiments.ConclusionThe biosynthesis, drug resistance mechanism, and combination of artemisinin have become more popular than before. Studies on artemisinin treating coronavirus disease 2019 (COVID-19) have been carried out, and good research results have been obtained.

Abstract Background Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies. Methods Anonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance. Results The agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516, p 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. Conclusions The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset.A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics.In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/.

Abstract Background Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the high resolution (gigapixel) of pathological images and the lack of annotation capabilities. To address these challenges, we propose a training strategy called deep-reverse active learning (DRAL) and atrous DenseNet (ADN) for pathological image classification. The proposed DRAL can improve the classification accuracy of widely used deep learning networks such as VGG-16 and ResNet by removing mislabeled patches in the training set. As the size of a cancer area varies widely in pathological images, the proposed ADN integrates the atrous convolutions with the dense block for multiscale feature extraction. Results The proposed DRAL and ADN are evaluated using the following three pathological datasets: BACH, CCG, and UCSB. The experiment results demonstrate the excellent performance of the proposed DRAL + ADN framework, achieving patch-level average classification accuracies (ACA) of 94.10%, 92.05% and 97.63% on the BACH, CCG, and UCSB validation sets, respectively. Conclusions The DRAL + ADN framework is a potential candidate for boosting the performance of deep learning models for partially mislabeled training datasets.

Flaviviruses, represented by Zika and dengue virus (ZIKV and DENV), are widely present around the world and cause various diseases with serious consequences. However, no antiviral drugs have been clinically approved for use against them. Azelnidipine (ALP) is a dihydropyridine calcium channel blocker and has been approved for use as an antihypertensive drug. In the present study, ALP was found to show potent anti-flavivirus activities in vitro and in vivo. ALP effectively prevented the cytopathic effect induced by ZIKV and DENV and inhibited the production of viral RNA and viral protein in a dose-dependent manner. Moreover, treatment with 0.3 mg/kg of ALP protected 88.89% of mice from lethal challenge. Furthermore, using the time-of-drug-addition assay, the enzymatic inhibition assay, the molecular docking, and the surface plasmon resonance assay, we revealed that ALP acted at the replication stage of the viral infection cycle by targeting the viral RNA-dependent RNA polymerase. These findings highlight the potential for the use of ALP as an antiviral agent to combat flavivirus infections.

Enterovirus infections can cause hand, foot, and mouth disease (HFDM), aseptic meningitis, encephalitis, myocarditis, and acute flaccid myelitis, leading to death of infants and young children. However, no specific antiviral drug is currently available for the treatment of this type of infection. The Unites States and United Kingdom health authorities recently approved a new antiviral drug, molnupiravir, for the treatment of COVID-19. In this study, we reported that molnupiravir (EIDD-2801) and its active form, EIDD-1931, have broad-spectrum anti-enterovirus potential. Our data showed that EIDD-1931 could significantly reduce the production of EV-A71 progeny virus and the expression of EV-A71 viral protein at non-cytotoxic concentrations. The results of the time-of-addition assay suggest that EIDD-1931 acts at the post-entry step, which is in accordance with its antiviral mechanism. The intraperitoneal administration of EIDD-1931 and EIDD-2801 protected 1-day-old ICR suckling mice from lethal EV-A71 challenge by reducing the viral load in various tissues of the infected mice. The pharmacokinetics analysis indicated that the plasma drug concentration overwhelmed the EC50 for enteroviruses, suggesting the clinical potential of molnupiravir against enteroviruses. Thus, molnupiravir along with its active form, EIDD-1931, may be a promising drug candidate against enterovirus infections.

Real-time and effective early warning of highway engineering construction sites is the key to ensuring the safety of highway engineering construction. At present, highway engineering construction safety early warning is limited by the experience of relevant personnel at the site and the dynamic changes of the project site environment. Therefore, the creation of a more active, smarter, and more effective real-time early warning model for construction safety is a strong complement to current research and has important theoretical and practical implications. The Internet of Things is the third wave of the information industry after computers, the Internet, and mobile communication networks. It is of great significance to promote the development of science and technology, economic growth, and social progress. Aiming at the shortcomings of the inadequate safety management methods for highway engineering construction in China, the inefficient efficiency of safety production supervision and management, and the emphasis on single and sporty supervision methods, a real-time early warning model for highway engineering construction safety based on the Internet of Things technology was constructed. By quantifying, scoring, and statistics of the safety situation during the construction process, the model achieves the goals of real-time monitoring, early warning, and handling hidden safety hazards. It overcomes problems such as untimely and unscientific safety issues in the past and effectively improves China’s highway engineering construction. The experimental comparison between the real-time early warning model and the traditional early warning model in this paper shows that the accuracy of the early warning model proposed in this paper is improved by nearly 5%, and the false alarm rate is reduced by nearly 4%.

The development of vessels can provide important information about the growth status of animal embryos. It is, therefore, important to automatically locate the deformed vessel branches from the embryo images. However, very few vessel detectors can accurately locate all vessel branches when the captured images are low quality and the implied vessel shapes are complex. In this study, a new framework consisting of vessel region extraction and snake shape optimisation is proposed. The main contribution in this detector is a novel open snake model based on the global guidance field and deformation template initialisation. Experimental results on a specific application of an embryo vessel database [Database and source codes: https://github.com/wcxie/Egg‐embryro‐vessel‐location/.] demonstrate that the proposed algorithm not only locates the vessel shape properly but also obtains the orientations of embryo vessel branches accurately. Comparison to traditional guidance fields and the active appearance model illustrates the effectiveness and competitiveness of the proposed model.

Human epithelial type 2 (HEp-2) cell images play an important role for the detection of antinuclear autoantibodies in autoimmune diseases. As the HEp-2 cell has hundreds of different patterns, none of currently available HEp-2 datasets contain all of the types. Therefore, existing automatic processing systems for HEp-2 cells, e.g., cell segmentation and classification, needs to be transferred between different data sets. However, the performances of transferred system often dramatically decrease, especially when transferring supervised-approaches, e.g., deep learning network, from large dataset to the small but similar ones. In this paper, a novel transfer-learning framework using generative adversarial networks (cC-GAN) is proposed for robust segmentation of different HEp-2 datasets. The proposed cC-GAN tries to solve the overfitting problem of most deep learning networks and improves their transfer-capacity. An improved U-net, so-called Residual U-net (RU-net), is developed to work as the generator for cC-GAN model. The cC-GAN was first trained and tested using I3A dataset and then directly evaluated using MIVIA dataset, which is much smaller than I3A. The segmentation result demonstrates the excellent transferring-capacity of our cC-GAN framework, i.e., a new state-of-the-art segmentation accuracy of 75.27% was achieved on MIVIA without finetuning.

A graphene oxide (GO) solution was irradiated by a Xenon lamp to form reduced graphene oxide (RGO). After irradiation, the epoxy, the carbonyl and the hydroxy groups are gradually removed from GO, resulting in an increase of sp2 π-conjugated domains and defect carbons with holes for the formed RGO. The RGO conductivity increases due to the restoration of sp2 π-conjugated domains. The photocatalytic activity of EY-RGO/Pt for hydrogen evolution was investigated with eosin Y (EY) as a sensitizer of the RGO and Pt as a co-catalyst. When the irradiation time is increased from 0 to 24 h the activity rises, and then reaches a plateau. Under optimum conditions (pH 10.0, 5.0 × 10−4 mol L−1 EY, 10 μg mL−1 RGO), the maximal apparent quantum yield (AQY) of EY-RGO24/Pt for hydrogen evolution rises up to 12.9% under visible light irradiation (λ ≥ 420 nm), and 23.4% under monochromatic light irradiation at 520 nm. Fluorescence spectra and transient absorption decay spectra of the EY-sensitized RGO confirm that the electron transfer ability of RGO increases with increasing irradiation time. The adsorption quantity of EY on the surface of RGO enhances, too. The two factors ultimately result in an enhancement of the photocatalytic hydrogen evolution over EY-RGO/Pt with increasing irradiation time. A possible mechanism is discussed.

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

TiO2 photocatalysts doped with alkaline-earth metal ions were prepared by the impregnation and coprecipitation methods. The sample were characterized by XRD, XPS and IR spectroscopy. Their activities were evaluated by the photocatalytic production of hydrogen. The activities of the doped photocatalysts dopended on the size of the dopant ions and the dopingmethod. The optimum molar contents of dopant ions Be2+, Mg2+, Ca2+, Sr2+, Ba2+ were 1.25, 1.25, 2.25, 2.25 and 2.25 at. %, respectively. The optimum calcination temperature and time were 400 °C and 1 h.