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The remaining creep life of the reheat section pipeline of low hardness P91 steel replaced in a certain power plant was studied. The rupture behavior of the joint was studied using metallographic microscope (OM), scanning electron microscope (SEM), and Electron Back Scatter Diffraction (EBSD), and the creep life was predicted. The results show that the rupture behavior of P91 steel and its joint during service is related to external stress. When the stress is high, the failure position of the joint is concentrated at the lowest hardness position, dominated by low strength martensitic deformation cracking; When the stress is low, the joint fails from FGHAZ in the form of Type IV cracking, resulting in a combination of martensitic structure degradation and pore nucleation. Type IV cracking has a significant impact on the life of joints, and the polygonal line OSD method based on fracture mode for life prediction can effectively improve the accuracy of material life prediction.

In order to improve the wound repair environment, this research has successfully developed a new multifunctional hydrogel dressing, which has strong adaptability and can accelerate wound healing. Pioneering the development of metal-ion-controlled hydrogel dressings, this research integrates dopamine and imidazole double crosslinked networks with metal-ion coordination. The resulting hydrogel dressing exhibits a notable antibacterial effect and exceptional mechanical properties, withstanding pressures of up to 12 kPa, tensions of 25 kPa, and maintaining skin adhesion at 6 kPa. Furthermore, the dressing can self-heal within only 7–8 s post-injection. Impressively, the hydrogel achieves complete biodegradation within a short timeframe (37 h). Notably, the use of various metal ions facilitates painless peeling during the degradation period, perfectly aligning with the requirements of an ideal wound dressing. This study has made significant progress in the fields of trauma repair and materials, providing strong solutions for dealing with harsh post-traumatic environments.

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to the long and complex process of drug research and development (R&D). With the advancement of experimental technology and computer hardware, artificial intelligence (AI) has recently emerged as a leading tool in analyzing abundant and high-dimensional data. Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D. Driven by big data in biomedicine, AI has led to a revolution in drug R&D, due to its ability to discover new drugs more efficiently and at lower cost. This review begins with a brief overview of common AI models in the field of drug discovery; then, it summarizes and discusses in depth their specific applications in various stages of drug R&D, such as target discovery, drug discovery and design, preclinical research, automated drug synthesis, and influences in the pharmaceutical market. Finally, the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

A novel self-gelatinizing powder was designed to accelerate wound healing through enhanced hemostasis and tissue recovery. Significantly, this research addresses the critical need for innovative wound management solutions by presenting a novel approach. Carboxymethylcellulose calcium (CMC-Ca) was synthesized using an ion exchange method, and lysine (Lys) was integrated through physical mixing to augment the material’s functional characteristics. The prepared powder underwent comprehensive evaluation for its self-gelling capacity, gelation time, adhesion, swelling rate, coagulation efficiency, hemostatic effectiveness, and wound healing promotion. Results indicate that the self-gelatinizing powder exhibited remarkable water absorption capabilities, absorbing liquid up to 30 times its weight and achieving rapid coagulation within 3 min. The inclusion of Lys notably enhanced the powder’s gel-forming properties. The gelation time was determined to be within 4 s using a rotational rheometer, with the powder rapidly forming a stable gel on the skin surface. Furthermore, in a mouse skin injury model, near-complete skin recovery was observed within 14 days, underscoring the powder’s impressive self-healing attributes and promising application prospects in wound management.

Heavy metal (HM) stress is a non-negligible abiotic stress that seriously restricts crop yield and quality, while the sprout stage is the most sensitive to stress and directly impacts the growth and development of the later stage. Melatonin (N-acetyl-5-methoxytryptamine), as an exogenous additive, enhances stress resistance due to its ability to oxidize and reduce. However, few reports on exogenous melatonin to tiger nuts under HM stress have explored whether exogenous melatonin enhances plants' resistance to heavy metals. Here, “Jisha 2″ was used as material, with a stress concentration of 5 mg/L and 100 μmol/L of CdCl2 to explore whether exogenous melatonin enhances plant resistance and molecular mechanism. The result revealed that stress limits growth, while melatonin alleviated the sprout damage under stress from the phenotypes. Moreover, stress-enhanced reactive oxygen species (ROS) accumulation and membrane lipid peroxidation, while melatonin-increased ROS reduce damage via the analysis of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) and malondialdehyde (MDA) content, hydrogen peroxide (H2O2), superoxide anion (O2-), and Electrolyte leakage (El). Further results indicated that HM leads to DNA damage while exogenous melatonin will repair the damage by analyzing random amplified polymorphic DNA (RAPD), DNA cross-linking, 8-hydroxy-20-deoxyguanine level, and relative density of apurinic sites. Furthermore, gene expression in the DNA-repaired pathway exhibited similar results. These results applied that exogenous melatonin released the hurt caused by HM stress, with DNA repair and ROS balance serving as candidate pathways. This study elucidated the mechanism of melatonin's influence and provided theoretical insights into its application in tiger nuts.

A random aggregate (RA) model consisting of mortar, aggregate, and the interfacial transition zone (ITZ) was established to investigate the effect of existing cracks on the tensile properties of concrete. Based on this, a method for establishing a geometric model of cracked concrete was proposed by inserting 0-depth cohesive elements into the RA model. The influence of crack length, width, and area on the tensile properties of concrete were studied using the established cracked concrete model. In addition, the key parameters of bond strength and fracture energy of the mortar and ITZ regions were tested. The cement is ordinary Portland cement with a strength grade of 42.5, and the sand is medium sand. The fracture energy tests were conducted using a displacement-controlled loading model with a loading rate of 0.4 mm/min, while the tensile strength tests were conducted using a stress-controlled loading method with a loading rate of 0.06 MPa/s. The feasibility and accuracy of the numerical method used were verified by comparing the results with those of a finite compression simulation. The numerical results show that as the crack length increases, the tensile strength and elastic modulus of concrete gradually decrease, but if the crack length is 0.47 h, the elastic modulus of concrete shows an abnormal increase. The range of the crack's influence on the tensile performance of concrete is between 50 mm and 100 mm. If the crack width is less than 0.55 mm, the tensile strength of concrete gradually decreases with the increase of crack width, and when the crack width is greater than 0.55 mm, the tensile strength stabilizes.

Abstract Large-scale COVID-19 vaccinations are currently underway in many countries in response to the COVID-19 pandemic. Here, we report, besides generation of neutralizing antibodies, consistent alterations in hemoglobin A1c, serum sodium and potassium levels, coagulation profiles, and renal functions in healthy volunteers after vaccination with an inactivated SARS-CoV-2 vaccine. Similar changes had also been reported in COVID-19 patients, suggesting that vaccination mimicked an infection. Single-cell mRNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) before and 28 days after the first inoculation also revealed consistent alterations in gene expression of many different immune cell types. Reduction of CD8+ T cells and increase in classic monocyte contents were exemplary. Moreover, scRNA-seq revealed increased NF-κB signaling and reduced type I interferon responses, which were confirmed by biological assays and also had been reported to occur after SARS-CoV-2 infection with aggravating symptoms. Altogether, our study recommends additional caution when vaccinating people with pre-existing clinical conditions, including diabetes, electrolyte imbalances, renal dysfunction, and coagulation disorders.

BackgroundNon-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.MethodsIn The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.ResultsWith the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.ConclusionThis study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy.

One of the most advanced, promising, and commercially viable research issues in the world of hydrogel dressing is gaining functionality to achieve improved therapeutic impact or even intelligent wound repair. In addition to the merits of ordinary hydrogel dressings, functional hydrogel dressings can adjust their chemical/physical properties to satisfy different wound types, carry out the corresponding reactions to actively create a healing environment conducive to wound repair, and can also control drug release to provide a long-lasting benefit. Although a lot of in-depth research has been conducted over the last few decades, very few studies have been properly summarized. In order to give researchers a basic blueprint for designing functional hydrogel dressings and to motivate them to develop ever-more intelligent wound dressings, we summarized the development of functional hydrogel dressings in recent years, as well as the current situation and future trends, in light of their preparation mechanisms and functional effects.

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.

In the original publication there are few errors in Figure 1. The correct Figure 1 is provided in this correction

Abstract Emerging and re-emerging RNA viruses occasionally cause epidemics and pandemics worldwide, such as the on-going outbreak of the novel coronavirus SARS-CoV-2. Herein, we identified two potent inhibitors of human DHODH, S312 and S416, with favorable drug-likeness and pharmacokinetic profiles, which all showed broad-spectrum antiviral effects against various RNA viruses, including influenza A virus, Zika virus, Ebola virus, and particularly against SARS-CoV-2. Notably, S416 is reported to be the most potent inhibitor so far with an EC50 of 17 nmol/L and an SI value of 10,505.88 in infected cells. Our results are the first to validate that DHODH is an attractive host target through high antiviral efficacy in vivo and low virus replication in DHODH knock-out cells. This work demonstrates that both S312/S416 and old drugs (Leflunomide/Teriflunomide) with dual actions of antiviral and immuno-regulation may have clinical potentials to cure SARS-CoV-2 or other RNA viruses circulating worldwide, no matter such viruses are mutated or not.

Among the three existing targeted gene editing technologies, zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regularly interspaced short palindromic repeats-CRISPR-associated 9 (CRISPR-Cas9), the latter is widely used owing to its simplicity, efficiency, and low cost. Here, we routinely infected A172 and U251 cells with lentiviral vectors, in which aquaporin-8 (AQP8) was knocked out using CRISPR/Cas9. Our results indicated that cryopreservation did not significantly alter the viral infection efficiency, but influenced AQP8 expression in the infected cells at both protein and mRNA levels compared with the non-cryopreserved samples. Further, AQP8 expression at protein and mRNA levels in recovered cryopreserved infected cells did not significantly differ from those in the blank and negative controls, indicating that the lentivirus was still infectious at low temperatures. However, it failed to release the AQP8-targeting guide RNA in the infected cells, or the guide RNA was released, but underwent changes that caused it to malfunction in the cells with CRISPR/Cas9-mediated AQP8 knock-out. Our findings possibly provide some insights into the reliability of lentiviruses as CRISPR/Cas9 vectors.

Background: Exosomes are well-known natural nanovesicles, that represent one of the recently discovered modes of intercellular communication due to their ability to transmit cellular components. Exosomes have been reported to have potential as natural vectors for carrying functional small RNAs and delivering chemotherapeutic agents to diseased cells. In this study, we aimed to investigate the role of exosomes in carrying miRNA for targeting tumor cells.Methods: We present a novel method for engineering exosomes with functional miR-317b-5b to target tumor cells. MiR-317b-5b exerts its anti-tumor function via its expression in tumors. RT-qPCR was performed to assess the levels of miR-371b-5p, FUT-4. Western blot was performed to measure the levels of CD9, CD81, and FUT-4 proteins. Confocal microscopy was used to observe the internalization of miR-317b-5b in tumor cells. CCK-8, EdU, flow cytometry, wound-healing migration and transwell assays were performed to evaluate cell viability, proliferation, migration, and invasion, respectively.Results: Our findings illustrated that miR-317b-5b-loaded engineered exosomes were internalized by tumor cells. MiR-317b-5b was overexpressed in tumor cells treated with miR-317b-5b-loaded engineered exosomes. The internalization of miR-317b-5b in tumor cells was accompanied by changes of cell viability, proliferation, apoptosis, and migratory and invasive capability. We found that miR-317b-5b-loaded engineered exosomes were presence in tumor tissue sections and miR-317b-5b was overexpressed in tumor tissues of osteosarcoma tumor-bearing mice infected with miR-317b-5b-loaded engineered exosomes. MiR-317b-5b-loaded engineered exosomes had the anti-tumor efficiency in vivo.Conclusion: Our findings show that miR-317b-5b-loaded engineered exosomes can be used as nanocarriers to deliver drug molecules such as miR-317b-5b both in vitro and in vivo to exert its anti-tumor functions.

In the study of the water–energy–food nexus (WEF nexus), the importance of the intensity and quantity index system has been widely recognized. In order to study the impact of WEF on the economy, this paper establishes an intensity index system and a quantity index system, taking account of the impact of environmental pollution. Using a DEA model and China’s provincial data from 2019, this paper calculated the efficiency of the WEF nexus with the developed intensity and quantity index systems. The results show that the efficiency is not high in areas with a high economic development level, and efficiency is not the lowest in areas with a relatively low economic development level. When considering environmental pollution, the efficiency of some provinces has increased significantly, indicating that the WEF nexus has not caused environmental damage and is conducive to sustainable economic development. In the two intensity index systems, the efficiency of the production system is significantly lower than that of the consumption system, indicating that there is a serious waste of cultivated land per capita. Compared with the intensity index system, the efficiency of the quantity index system is low, and the polarization is obvious. A high level of GDP does not mean a high level of economic development. There may be a low level of resource utilization technology or environmental pollution underlying it. It is unscientific to evaluate local economic development only by GDP. When evaluating the urban economy and national economy, we should conduct an overall study of WEF and reasonably allocate WEF resources, which will not only help to alleviate the current situation of resource shortage in various countries but also effectively promote the coordinated development of national and regional economies. At the same time, environmental protection should also be taken into account. Compared with the economic development model of developing the economy first and then solving environmental problems, developing and solving at the same time is more conducive to the sustainable development of the national economy.

To solve the problem of user privacy disclosure caused by attacks on anonymous areas in spatial generalization privacy protection methods, a K and P Dirichlet Retrieval (KPDR) method based on k-anonymity mechanism is proposed. First, the Dirichlet graph model is introduced, the same kind of information points is analyzed by using the characteristics of Dirichlet graph, and the anonymous set of users is generated and sent to LBS server. Second, the relationship matrix is generated, and the proximity relationship between the user position and the target information point is obtained by calculation. Then, the private information retrieval model is applied to ensure the privacy of users’ target information points. Finally, the experimental results show that the KPDR method not only satisfies the diversity of l3/4, but also increases the anonymous space, reduces the communication overhead, ensures the anonymous success rate of users, and effectively prevents the disclosure of user privacy.

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Water molecules play an important role in modeling protein-ligand interactions. However, traditional molecular docking methods often ignore the impact of the water molecules by removing them without any analysis or keeping them as a static part of the proteins or the ligands. Hence, the accuracy of the docking simulations will inevitably be damaged. Here, we introduce a multi-body docking program which incorporates the fixed or the variable number of the key water molecules in protein-ligand docking simulations. The program employed NSGA II, a multi-objective optimization algorithm, to identify the binding poses of the ligand and the key water molecules for a protein. To this end, a force-field-based hydration-specific scoring function was designed to favor estimate the binding affinity considering the key water molecules. The program was evaluated in aspects of the docking accuracy, cross-docking accuracy, and screening efficiency. When the numbers of the key water molecules were treated as fixed-length optimization variables, the docking accuracy of the multi-body docking program achieved a success rate of 80.58% for the best RMSD values for the recruit of the ligands smaller than 2.0 Å. The cross-docking accuracy was investigated on the presence and absence of the key water molecules by four protein targets. The screening efficiency was assessed against those protein targets. Results indicated that the proposed multi-body docking program was with good performance compared with the other programs. On the other side, when the numbers of the key water molecules were treated as variable-length optimization variables, the program obtained comparative performance under the same three evaluation criterions. These results indicated that the multi-body docking with the variable numbers of the water molecules was also efficient. Above all, the multi-body docking program developed in this study was capable of dealing with the problem of the water molecules that explicitly participating in protein-ligand binding.