ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Global trends in PANoptosis research: bibliometrics and knowledge graph analysis.

PANoptosis has recently been discovered as a new type of cell death. PANoptosis mainly refers to the significant interaction among the three programmed cell death pathways of apoptosis, necroptosis, and pyroptosis. Despite this, only a few studies have examined the systematic literature in this area. By analyzing the bibliometric data for PANoptosis, we can visualize the current hotspots and predicted trends in research. This study analyzed bibliometric indicators using the Histcite Pro 2.0 tool, which searches the Web of Science for PANoptosis literature published between 2016 and 2022. A bibliometric analysis was performed using Histcite Pro 2.0, while research trends and hotspots were visualized using VOSviewer, CiteSpace and BioBERT. The output of related literature was low in the four years from the first presentation of PANoptosis in 2016 to 2020. The volume of relevant literature grew exponentially between 2020 and 2022. The United States and China play a leading role in this field. Although China started late, its research in this field is developing rapidly. As research progressed, more focus was placed on the relationship between PANoptosis and pyroptosis, as well as apoptosis and necrosis. Now is a rapid development stage of PANoptosis research. Most of the research focuses on the cellular level, and the focus is more on the treatment of tumor-related diseases. The current focus of this area is PANoptosis mechanisms in cancer and inflammation. It can be seen from the burst analysis of keywords that caspase1 and host defense have consistently been research hotspots in the field of PANoptosis, while the frequency of NLRC4, causes of autoinflammation, recognition, NLRP3, and Gasdermin D has gradually increased, all of which have become research hotspots in recent years. Finally, we used the BioBERT biomedical language model to mine the most documented genes and diseases in the PANoptosis field articles, pointing out the direction for subsequent research steps. According to a bibliometric analysis, researchers have shown an increased interest in PANoptosis over the past few years. Researchers initially focused on the molecular mechanism of PANoptosis and pyroptosis, apoptosis, and necroptosis. The role of PANoptosis in diseases and conditions such as inflammation and tumors is one of the current research hotspots in this area. The focus is more on treating inflammation-related diseases, which will become the key development direction of future research.

Helicobacter pylori CagA Promotes the Formation of Gallstones by Increasing the Permeability of Gallbladder Epithelial Cells.

BACKGROUND: The formation of gallstones is often accompanied by chronic inflammation, and the mechanisms underlying inflammation and stone formation are not fully understood. Our aim is to utilize single-cell transcriptomics, bulk transcriptomics, and microbiome data to explore key pathogenic bacteria that may contribute to chronic inflammation and gallstone formation, as well as their associated mechanisms. METHODS: scRNA-seq data from a gallstone mouse model were extracted from the Gene Expression Omnibus (GEO) database and analyzed using the FindCluster() package for cell clustering analysis. Bulk transcriptomics data from patients with gallstone were also extracted from the GEO database, and intergroup functional differences were assessed using GO and KEGG enrichment analysis. Additionally, 16S rRNA sequencing was performed on gallbladder mucosal samples from asymptomatic patients with gallstone (n = 6) and liver transplant donor gallbladder mucosal samples (n = 6) to identify key bacteria associated with stone formation and chronic inflammation. Animal models were constructed to investigate the mechanisms by which these key pathogenic bacterial genera promote gallstone formation. RESULTS: Analysis of scRNA-seq data from the gallstone mouse model (GSE179524) revealed seven distinct cell clusters, with a significant increase in neutrophil numbers in the gallstone group. Analysis of bulk transcriptomics data from patients with gallstone (GSE202479) identified chronic inflammation in the gallbladder, potentially associated with dysbiosis of the gallbladder microbiota. 16S rRNA sequencing identified Helicobacter pylori as a key bacterium associated with gallbladder chronic inflammation and stone formation. CONCLUSIONS: Dysbiosis of the gallbladder mucosal microbiota is implicated in gallstone disease and leads to chronic inflammation. This study identified H. pylori as a potential key mucosal resident bacterium contributing to gallstone formation and discovered its key pathogenic factor CagA, which causes damage to the gallbladder mucosal barrier. These findings provide important clues for the prevention and treatment of gallstones.

Metadata-Private Resource Allocation in Edge Computing Withstands Semi-Malicious Edge Nodes.

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals.

Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

Initial Response of Pentaerythritol Tetranitrate (PETN) under the Coupling Effect of Preheating, Shock and Defect via the Molecular Dynamics Simulations with the Multiscale Shock Technique Method.

The initial response of PETN under the coupling of preheating, impact and defects was simulated by Multiscale Shock Technique (MSST) method and molecular dynamics. The temperature change of PETN during impact compression can be divided into three stages: (1) the elastoplastic change of the system caused by initial compression; (2) part of PETN decomposes and releases energy to raise temperature; (3) a secondary chemical reaction occurs, resulting in rapid temperature rise. Under the given conditions, a higher initial preheating temperature will lead to faster decomposition of PETN; The existence of defects will accelerate the decomposition of PETN molecules; Coupling the highest preheating temperature with defects will lead to the fastest decomposition of PETN molecules, while in the defect-free PETN system with a preheating temperature of 300 K, the decomposition of PETN molecules is the slowest. For the case of Us = 8 km·s-1, the effect of defects on the initial PETN reaction is greater than the initial preheating temperature; When the impact velocity is greater than 9 km·s-1, the impact velocity is an important factor affecting the decomposition of PETN molecules. For Us = 10 km·s-1, NO2 is the main initial product in the defective PETN crystal, while in the perfect PETN crystal, it is the combination of NO2 and HONO. The chemical reaction kinetics analysis shows that the preheating temperature and defects will accelerate the decomposition of PETN. The higher the preheating temperature, the faster the decomposition of PETN. For the case of Us = 7 km·s-1, 8 km·s-1 and 9 km·s-1, the existence of defects will increase the decomposition rate by more than 50% regardless of the initial preheating temperature. In the case of Us = 10 km·s-1, the improvement of decomposition rate by defects is not as significant as the initial preheating temperature.

3-Silaazetidine: An Unexplored yet Versatile Organosilane Species for Ring Expansion toward Silaazacycles.

Small-ring silacycles are important organosilane species in main-group chemistry and have found numerous applications in organic synthesis. 3-Silaazetidine, a unique small silacycle bearing silicon and nitrogen atoms, has not been adequately explored due to the lack of a general synthetic scheme and its sensitivity to air. Here, we describe that 3-silaazetidine can be easily prepared in situ from diverse air-stable precursors (RSO2NHCH2SiR12CH2Cl). 3-Silaazetidine shows excellent functional group tolerance in a palladium-catalyzed ring expansion reaction with terminal alkynes, giving 3-silatetrahydropyridines and diverse silaazacycle derivatives, which are promising ring frameworks for the discovery of Si-containing functional molecules.

A study on the potential of digital economy in reducing agricultural carbon emissions.

Agriculture is a significant source of carbon emissions, which have a substantial environmental impact. The digital economy plays a vital role in mitigating these emissions through innovative digital solutions. As a leading agricultural nation, China faces substantial pressure to reduce its agricultural carbon emissions(ACE). This paper aims to thoroughly examine the relationship between the growth of the rural digital economy and ACE. To achieve this, we utilize an extensive panel dataset covering China's provinces from 2011 to 2020, analyzing the dynamic and spatial effects of digital economy development on ACE. The key findings of this research are as follows: (1) The rapid expansion of the digital economy significantly reduces ACE. (2) The impact of digital economic development on lowering ACE varies spatially, with a clear progression from eastern to western regions. (3) The digital economy helps reduce ACE through three specific channels: fostering technological innovation, enhancing scale efficiency management, and providing agricultural financial incentives. Based on these findings, this study proposes policy recommendations to improve digital infrastructure, promote balanced regional development in the digital economy, and optimize the management of agricultural science and technology. These policy insights aim to transform agriculture and achieve the goal of reducing ACE, thereby contributing to broader environmental sustainability.

Global Trends in mHealth and Medical Education Research: Bibliometrics and Knowledge Graph Analysis.

Background: Mobile health (mHealth) is an emerging mobile communication and networking technology for health care systems. The integration of mHealth in medical education is growing extremely rapidly, bringing new changes to the field. However, no study has analyzed the publication and research trends occurring in both mHealth and medical education. Objective: The aim of this study was to summarize the current application and development trends of mHealth in medical education by searching and analyzing published articles related to both mHealth and medical education. Methods: The literature related to mHealth and medical education published from 2003 to 2023 was searched in the Web of Science core database, and 790 articles were screened according to the search strategy. The HistCite Pro 2.0 tool was used to analyze bibliometric indicators. VOSviewer, Pajek64, and SCImago Graphica software were used to visualize research trends and identify hot spots in the field. Results: In the past two decades, the number of published papers on mHealth in medical education has gradually increased, from only 3 papers in 2003 to 130 in 2022; this increase became particularly evident in 2007. The global citation score was determined to be 10,600, with an average of 13.42 citations per article. The local citation score was 96. The United States is the country with the most widespread application of mHealth in medical education, and most of the institutions conducting in-depth research in this field are also located in the United States, closely followed by China and the United Kingdom. Based on current trends, global coauthorship and research exchange will likely continue to expand. Among the research journals publishing in this joint field, journals published by JMIR Publications have an absolute advantage. A total of 105 keywords were identified, which were divided into five categories pointing to different research directions. Conclusions: Under the influence of COVID-19, along with the popularization of smartphones and modern communication technology, the field of combining mHealth and medical education has become a more popular research direction. The concept and application of digital health will be promoted in future developments of medical education.

Deep learning based characterization of human organoids using optical coherence tomography.

Organoids, derived from human induced pluripotent stem cells (hiPSCs), are intricate three-dimensional in vitro structures that mimic many key aspects of the complex morphology and functions of in vivo organs such as the retina and heart. Traditional histological methods, while crucial, often fall short in analyzing these dynamic structures due to their inherently static and destructive nature. In this study, we leveraged the capabilities of optical coherence tomography (OCT) for rapid, non-invasive imaging of both retinal, cerebral, and cardiac organoids. Complementing this, we developed a sophisticated deep learning approach to automatically segment the organoid tissues and their internal structures, such as hollows and chambers. Utilizing this advanced imaging and analysis platform, we quantitatively assessed critical parameters, including size, area, volume, and cardiac beating, offering a comprehensive live characterization and classification of the organoids. These findings provide profound insights into the differentiation and developmental processes of organoids, positioning quantitative OCT imaging as a potentially transformative tool for future organoid research.

Heterogeneity and Functional Analysis of Cardiac Fibroblasts in Heart Development.

Background: As one of the major cell types in the heart, fibroblasts play critical roles in multiple biological processes. Cardiac fibroblasts are known to develop from multiple sources, but their transcriptional profiles have not been systematically compared. Furthermore, while the function of a few genes in cardiac fibroblasts has been studied, the overall function of fibroblasts as a cell type remains uninvestigated. Methods: Single-cell mRNA sequencing (scRNA-seq) and bioinformatics approaches were used to analyze the genome-wide genes expression and extracellular matrix genes expression in fibroblasts, as well as the ligand-receptor interactions between fibroblasts and cardiomyocytes. Single molecular in situ hybridization was employed to analyze the expression pattern of fibroblast subpopulation-specific genes. The Diphtheria toxin fragment A (DTA) system was utilized to ablate fibroblasts at each developmental phase. Results: Using RNA staining of Col1a1 at different stages, we grouped cardiac fibroblasts into four developmental phases. Through the analysis of scRNA-seq profiles of fibroblasts at 18 stages from two mouse strains, we identified significant heterogeneity, preserving lineage gene expression in their precursor cells. Within the main fibroblast population, we found differential expressions of Wt1, Tbx18, and Aldh1a2 genes in various cell clusters. Lineage tracing studies showed Wt1- and Tbx18-positive fibroblasts originated from respective epicardial cells. Furthermore, using a conditional DTA system-based elimination, we identified the crucial role of fibroblasts in early embryonic and heart growth, but not in neonatal heart growth. Additionally, we identified the zone- and stage-associated expression of extracellular matrix genes and fibroblast-cardiomyocyte ligand-receptor interactions. This comprehensive understanding sheds light on fibroblast function in heart development. Conclusion: We observed cardiac fibroblast heterogeneity at embryonic and neonatal stages, with preserved lineage gene expression. Ablation studies revealed their distinct roles during development, likely influenced by varying extracellular matrix genes and ligand-receptor interactions at different stages.

Hot spot formation and initial chemical reaction of PETN containing nanoscale spherical voids under high shock loading.

The initial response process of PETN containing nanoscale spherical cavities under impact loading was investigated using the ReaxFF-lg force field combined with the molecular dynamic method. The impact-induced void collapse process, hot spot formation and growth, and chemical reaction processes were determined. The hot spot formation goes through four stages: (1) overall temperature rise due to initial impact compression; (2) temperature rise on the upper surface of the void caused by local plastic deformation; (3) rapid temperature rise caused by molecules entering the interior of the void colliding with the downstream surface of the void; and (4) thermal diffusion between the hot spot and the surrounding region, resulting in a decrease in the temperature of the center of the hot spot and a slow increase in the temperature of the neighboring region. With weak impact, the void shape remains basically symmetric during the void collapse, and the void collapse is mainly caused by local plastic deformation. A strong impact will lead to a more intense material focusing. The void collapse caused by strong impact has a greater effect on the heating of the surrounding material, and the secondary compression formed by the collision between particles makes the hot spot area expand and the central region of the hot spot evolve into an approximate triangular cone. NO2 is produced in large quantities as the initial product during the void collapse to form the hot spot, indicating that the void activates the chemical reactivity of the PETN crystal.

ScRNA-seq and bulk RNA-seq reveal the characteristics of ferroptosis and establish a risk signature in cholangiocarcinoma.

Ferroptosis is a recently discovered mode of cell death that inhibits tumor growth. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity and the immune microenvironment at the single-cell level. We used CIBERSORT to identify cellular immune scores and found that monocytes had significantly infiltrated and were correlated with prognosis in cholangiocarcinoma. scRNA-seq data were extracted from the Gene Expression Omnibus database, and the FindCluster() package was used for cell cluster analysis, which obtained 21 cell clusters, and there was increased TNFSF13B-TFRC intercellular communication between monocytes and cholangiocytes. A weighted correlation network analysis was performed with the WGCNA package to obtain monocyte-related gene modules. Univariate and multivariate Cox analyses were then performed to further establish the signature, and the reliability of the signature was assessed by receiver operating characteristic curve and decision curve analysis. A nomogram signature based on the Kaplan-Meier survival analysis was established. We found that the communication between monocytes and malignant cells in cholangiocarcinoma may be a regulatory factor of ferroptosis in cancer cells. The prognostic stratification system of the three-gene signature related to monocytes and ferroptosis can accurately assess the prognostic risk for cholangiocarcinoma.

ANP32 Family as Diagnostic, Prognostic, and Therapeutic Biomarker Related to Immune Infiltrates in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, with high incidence and mortality rate. There is an urgent need to identify effective diagnostic and prognostic biomarkers for HCC. Members of the acidic leucine-rich nucleophosphoprotein 32 (ANP32) family, which mainly includes ANP32A, ANP32B, and ANP32E, are abnormally expressed and have prognostic value in certain cancers. However, the diagnostic, prognostic, and therapeutic value of ANP32 family members in HCC has not yet been fully studied. In this study, we identified the diagnostic and prognostic value of ANP32 family members in HCC. Transcriptome data from public databases, such as the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, suggested that ANP32A, ANP32B, and ANP32E were upregulated in HCC tissues, and high expression of ANP32 family members was associated with advanced pathologic stage and histologic grade. Our immunohistochemistry and western blot results further verified the differential expression of ANP32 family members. ANP32A, ANP32B, and ANP32E had an outstanding diagnostic potential. Survival analysis of HCC patients in TCGA databases demonstrated that ANP32A, ANP32B, and ANP32E were associated with poor overall survival (OS) and disease-specific survival (DSS). Univariate and multivariate Cox analyses suggested the capability of ANP32B and ANP32E to independently predict the OS and DSS of HCC patients. Gene set enrichment analysis (GSEA) showed that ANP32 family members were associated with immune response, epidermal cell differentiation, and stem cell proliferation. Expression of ANP32 family members was associated with immune cell infiltration and immune status in the tumor microenvironment of HCC, and patients with high ANP32 family expression had poor sensitivity to immunotherapy. Finally, we identified potential chemotherapy drugs for HCC patients with high ANP32 family expression by CellMiner database. This study suggested the diagnostic, prognostic, and therapeutic roles of the ANP32 family in HCC patients, providing potential therapeutic targets for HCC.

ReaxFF Molecular Dynamics Simulation of Hydrostatic and Uniaxial Compression of Nitrate Energetic Materials.

The physical and chemical properties of typical nitrate energetic materials under hydrostatic compression and uniaxial compression were studied using the ReaxFF/lg force field combined with the molecular dynamics simulation method. Under hydrostatic compression, the P-V curve and the bulk modulus (B 0) obtained using the VFRS equation of state show that the compressibility of the three crystals is nitroglycerine (NG) > erythritol tetranitrate (ETN) > 2,3-bis-hydroxymethyl-2,3-dinitro-1,4-butanediol tetranitrate (NEST-1). The a- and c-axis of ETN are easy to compress under the action of hydrostatic pressure, but the b-axis is not easy to compress. The b-axis of NEST-1 is the most compressible, while the a- and c-axis can be compressed slightly when the initial pressure increases and then remains unchanged afterward. The a-, b-, and c-axes of NG all have similar compressibilities. By analyzing the change trend of the main bond lengths of the crystals, it can be seen that the most stable of the three crystals is the N-O bond and the largest change is in the O-NO2 bond. The stability of the C-O bond shows that the NO3 produced by nitrates is not from the C-O bond fracture. Under uniaxial compression, the stress tensor component, the average principal stress, and the hydrostatic pressure have similar trends and amplitudes, indicating that the anisotropy behaviors of the three crystals ETN, NEST-1, and NG are weak. There is no significant correlation between maximum shear stress and sensitivity. The maximum shear stresses τ xy and τ yz of the ETN in the [010] direction are 1.5 GPa higher than τ xz . However, the maximum shear stress of NG shows irregularity in different compression directions, indicating that there is no obvious correlation between the maximum shear stress and sensitivity.

Analysis of Chemical Reaction Process after Pentaerythritol Tetranitrate Hot Spot Ignition.

In this paper, ReaxFF force field combined with molecular dynamics method was used to study the ignition, deflagration, and detonation of pentaerythritol tetranitrate (PETN) induced by hot spots. The hot spot is 5.6% of the total volume. When the hot spot temperature is 1000 K, the deflagration and detonation of PETN cannot be observed in the simulation time of 200 ps. When the hot spot temperature is 2000 K, it corresponds to the heating time of 20 to 50 ps, deflation and detonation were observed. During hot spot ignition, the products of decomposition of the condensed phase PETN are dominated by NO2 and HONO. The energy required for the C-C bond and C-ONO2 bond cleavage in PETN is high, resulting in only a small amount of CH2O and NO3 during the reaction. Small nitrogen-containing molecules (such as NO2, NO3, HONO, HNO3, etc.) mainly exist during thermal equilibrium, while the number of N2 increases sharply during the thermal runaway stage, and a small amount of NH3 and NH2 are also produced. H2O molecules are formed before CO2 and N2 are produced, and the number always dominates. During the thermal runaway, the entire system can maintain a spontaneous reaction, resulting in a sharp rise in temperature of about 2500 K in 20 ps. During this phase, the catalytic effect of H2O accelerates the formation of CO2 and N2 due to the near Chapman-Jouguet point in the crystal. PETN is a weak oxygen balance explosive that results in a small amount of CO and H2 production during the thermal instability phase. When the reaction is balanced, the relative molecular mass is close to or exceeds that of PETN. The product is only less than 1% of the total mass fraction, while the small molecule product is as high as 78%, and some relative molecular masses are [75,225]. The intermediates account for about 21%. Rapid and complex reaction events make it difficult to accurately predict the structure of these intermediates by existing experiments and calculations, which will be the focus of future research.

High expression of fibronectin 1 indicates poor prognosis in gastric cancer.

Fibronectin 1 (FN1) is involved in the occurrence and development of various tumors and is upregulated in multiple cancer types. FN1 has been demonstrated to promote cell proliferation and migration in gastric cancer cell lines. However, the relationship between the expression of FN1 and clinicopathological factors and prognosis is not clear in gastric cancer (GC). The aim of the present study was to investigate the association between FN1 expression and clinicopathology and prognosis of gastric cancer. In this study, 17 publicly available GC cohorts (n=2,376) with gene expression data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine databases were tested. In addition, FN1 protein expression was validated by immunohistochemistry in a separate cohort (n=190). The meta-analysis results demonstrated an increase in FN1 expression at the protein and mRNA level in GC tissues, and the FN1 gene was highly expressed at the mRNA level in the advanced T stage (T2 + T3 + T4) group compared with that in the early T stage (T1) group. In addition, the expression of epithelial FN1 at the protein level was positively correlated with tumor size. FN1 expression at the protein and mRNA level was a predictor of poor prognosis following radical resection of GC. In conclusion, the expression of FN1 in GC tissues is upregulated compared with adjacent normal tissues, and it is a potential biomarker of poor prognosis in patients with GC.

Shock-Induced Hot Spot Formation and Spalling in 1,3,5-trinitroperhydro-1,3,5-triazine Containing a Cube Void.

The initial reaction mechanism of energetic materials under impact loading and the role of crystal properties in impact initiation and sensitivity are still unclear. In this paper, we report reactive molecular dynamics simulations of shock initiation of 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) crystals containing a cube void. Shock-induced void collapse, hot spots formation and growth, as well as spalling are revealed to be dependent on the shock velocity. The void collapse times are 1.5 and 0.7 ps, for the shock velocity of 2 and 4 km·s-1, respectively. Results indicate that the initial hot spot formation consists of two steps: one is the temperature rise caused by local plastic deformation and the other is the temperature increase resulting from the collision of upstream and downstream particles during the void collapse. Whether hot spots will continue to grow or quench depends on sensitive balance between energy release caused by local physical and chemical reactions and various heat dissipation mechanisms. In our simulations, hot spot would grow for U p = 4 km·s-1; hot spot is weak to some extent for U p = 2 km·s-1. The tensile wave reflected by the shock wave after reaching the free surface causes the spalling, which depends on the initial shock velocity. Typical spalling occurs for the shock velocity 2 km·s-1, while the tensile wave induces the microsplit region in RDX crystals in the case of U p = 4 km·s-1. Chemical reactions are studied for Rankine-Hugoniot shock pressures P s = 14.4, 57.8 GPa. For the weak shock, there is almost no decomposition reaction of the RDX molecules near the spalling region. On the contrary, there are large number of small molecule products, such as H2O, CO2, NO2, and so forth, around the microsplit regions for the strong shock. The ruptures of N-NO2 bond are the main initial reaction mechanisms for the shocked RDX crystal and are not affected by shock strength, while the microsplit slows down the decomposition rate of RDX. The work in this paper can shed light on a thorough understanding of thermal ignition, hot spot growth, and other physical and chemical phenomena of energetic materials containing voids under impact loading.

The opposite role of alternatively spliced isoforms of LINC00477 in gastric cancer.

Background: Aberrant transcript alternative splicing is an important regulatory process closely connected with oncogenesis. Purpose: The objective of this study was to determine the phenotype and function of a novel long noncoding RNA (lncRNA) LINC00477 in gastric cancer. Patients and methods: The gastric cancer samples of 140 from Oncomine database and 17 from our own hospital, as well as three gastric cancer cell lines MKN-45, AGS and KATO III were used in this study. The expression of the spliced isoforms of LINC00477 were tested. The tumor effects of LINC00477 on gastric cancer were investigated in vitro and in vivo. The mechanism of LINC00477 interacted with aconitase 1 (ACO1) was further examined by RIP and pull down assay. Results: The overall expression of LINC00477 was reduced in gastric cancers compared to normal gastric tissues. The isoform 1 of LINC00477 was down-regulated while the isoform 2 was up-regulated in gastric cancer cells. The opposite role of isoforms 1 and 2 in the proliferation and migration of cancer cells in vitro and in vivo was observed. Furthermore, isoform 1 of LINC00477 was determined to interact with ACO1 and suppress the conversion ability from citrate to isocitrate by ACO1. Conclusion: we presented the important roles of the spliced isoforms of long noncoding RNA, LINC00477 in gastric carcinogenesis.