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Zhiqiang He,1 Huan Yang,1 Yufan Gu,1 Yuhan Xie,1 Jianan Wu,1 Chen Wu,1 Jiawei Song,1 Maofang Zhao,2,3 Da Zong,1 Wenlong Du,1 Jiaju Qiao,1 Yipeng Pang,1– 3 Yi Liu1– 3 1Department of Biophysics, School of Life Sciences, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2School of Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 3Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of ChinaCorrespondence: Yi Liu; Yipeng Pang, Department of Biophysics, School of Life Sciences, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China, Tel/Fax +86-0516-83262084, Email cbpeliuyinew@163.com; pangyipeng.xzmu@foxmail.comPurpose: Herein, an emerging drug delivery system was constructed based on zeolite imidazole backbone (ZIF-8) to improve antibacterial defects of nanosilver (AgNPs), such as easily precipitated and highly cytotoxic.Methods: The homogeneous dispersion of AgNPs on ZIF-8 was confirmed by UV-Vis spectroscopy, FTIR spectroscopy, particle size analysis, zeta potential analysis, and SEM. The appropriate AgNPs loading ratio on ZIF-8 was screened through the cell and antibacterial experiments based on biosafety and antibacterial performance. The optimal environment for AgNPs@ZIF-8 to exert antibacterial performance was probed in the context of bacterial communities under different acid-base conditions. The potential mechanism of AgNPs@ZIF-8 to inhibit the common clinical strains was investigated by observing the biofilm metabolic activity and the level of reactive oxygen species (ROS) in bacteria.Results: The successful piggybacking of AgNPs by ZIF-8 was confirmed using UV-Vis spectroscopy, FTIR spectroscopy, particle size analysis, zeta potential analysis, and SEM characterization methods. Based on the bacterial growth curve (0– 24 hours), the antibacterial ability of AgNPs@ZIF-8 was found to be superior to AgNPs. When the mass ratio of ZIF-8 and AgNPs was 1:0.25, the selection of AgNPs@ZIF-8 was based on its superior antimicrobial efficacy and enhanced biocompatibility. Notably, under weakly acidic bacterial microenvironments (pH=6.4), AgNPs@ZIF-8 demonstrated a more satisfactory antibacterial effect. In addition, experiments on biofilms showed that concentrations of AgNPs@ZIF-8 exceeding 1×MIC resulted in more than 50% biofilm removal. The nanomedicine was found to increase ROS levels upon detecting the ROS concentration in bacteria.Conclusion: Novel nanocomposites consisting of low cytotoxicity drug carrier ZIF-8 loaded with AgNPs exhibited enhanced antimicrobial effects compared to AgNPs alone. The pH-responsive nano drug delivery system, AgNPs@ZIF-8, exhibited superior antimicrobial activity in a mildly acidic environment. Moreover, AgNPs@ZIF-8 effectively eradicated pathogenic bacterial biofilms and elevated the intracellular level of ROS.Graphical Abstract: Keywords: MOF, ZIF-8, AgNPs, nanocomplex, ROS, pathogenic biofilm

Dan Zong, Ning Jiang, Jian-Hua Xu, De-Jun Wang, Huan-Feng Zhu, Li-Rong Wu, Cheng Chen, Li Yin, Xia HeJiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu Province 210009, People’s Republic of ChinaBackground: ZNF488 acts as an oncogene which promotes cell invasion and endows tumor cells stem cell capacity in nasopharyngeal carcinoma (NPC), but its correlation with clinicopathologic characteristics and patients’ survival in NPC remain undefined.Methods: In this study, 158 cases of confirmed NPC were subjected to immunohistochemistry staining for evaluating endogenous expression. Kaplan–Meier method and log-rank test were used to estimate the survival rates. The relationship between ZNF488 and clinicopathological characteristics was statistically calculated by chi-squared test, univariate and multivariate analysis. In addition, adhesion assay, MTT and colony formation assays were performed for measuring adhesion and proliferation capacity. Cell cycle analysis via flow cytometry was conducted to explore cell cycle distribution. Western blot was used to detect pathway protein levels, and the pFAK (Y397) kit was used for focal adhesion kinase (FAK) activation.Results: We demonstrated that high expression of ZNF488 was significantly correlated with locoregional failure (P=0.018) and distant metastasis (P=0.001). Patients with high ZNF488 expression had poorer overall survival (P

Xue He,1–3 Tiao Li,1–3 Naixin Kang,1–3 Huihui Zeng,1–3 Siying Ren,1–3 Dandan Zong,1–3 Jinhua Li,1–3 Shan Cai,1–3 Ping Chen,1–3 Yan Chen1–3 1Department of Respiratory Medicine, The Second Xiangya Hospital, 2Research Unit of Respiratory Disease, 3Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan, China Background: Cigarette smoke exposure is the most common risk factor for emphysema, which is one of the major pathologies of COPD. Protein arginine methyltransferase 6 (PRMT6) is a nuclear enzyme that specially catalyzes dimethylation of R2 in histone H3 (H3R2me2a). H3R2me2a prevents trimethylation of H3K4 (H3K4me3), which is located in the transcription start sites of genes in mammalian genomes. We attempted to determine the expression of PRMT6 in human samples, and investigate whether the upregulation of PRMT6 expression can attenuate the development of cigarette smoke extract (CSE)-induced emphysema. Further experiments were performed to elucidate the molecular mechanisms involved.Materials and methods: Human lung tissues were obtained from patients undergoing pneumonectomy for benign pulmonary lesions. BALB/c mice were treated with lentiviral vectors intratracheally and injected with CSE three times. The protein expression of PRMT6, H3R2me2a, and H3K4me3 in human and mouse samples, as well as B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein (Bax), and endothelial nitric oxide synthase (eNOS) in mice were detected in lung homogenates by Western blotting. The mRNA expression of cyclooxygenase-2, interleukin-6, Bcl-2, Bax, and eNOS in mice was measured by quantitative real-time polymerase chain reaction.Results: The expression of PRMT6 was significantly downregulated in the pulmonary parenchyma in smokers with COPD as well as in mice treated with CSE. Overexpression of PRMT6 was detected in the CSE + Lenti-PRMT6 group of mice, which reversed the expression of H3R2me2a and H3K4me3. Inflammation, apoptosis, and oxidative stress levels were severe in the CSE-treated emphysema mice compared with the control group, which was inhibited by the overexpression of PRMT6.Conclusion: The overexpression of PRMT6 might inhibit inflammation, apoptosis, and oxidative stress in CSE-induced emphysema mediated by H3R2me2a. Keywords: COPD, cigarette smoke extract, inflammation, apoptosis, oxidative stress, PRMT6

Abstract CRISPR‐based genomic‐imaging systems have been utilized for spatiotemporal imaging of the repetitive genomic loci in living cells, but they are still challenged by limited signal‐to‐noise ratio (SNR) at a non‐repetitive genomic locus. Here, an efficient genomic‐imaging system is proposed, termed CRISPR/Pepper‐tDeg, by engineering the CRISPR sgRNA scaffolds with the degron‐binding Pepper aptamers for binding fluorogenic proteins fused with Tat peptide derived degron domain (tDeg). The target‐dependent stability switches of both sgRNA and fluorogenic protein allow this system to image repetitive telomeres sensitively with a 5‐fold higher SNR than conventional CRISPR/MS2‐MCP system using “always‐on” fluorescent protein tag. Subsequently, CRISPR/Pepper‐tDeg is applied to simultaneously label and track two different genomic loci, telomeres and centromeres, in living cells by combining two systems. Given a further improved SNR by the split fluorescent protein design, CRISPR/Pepper‐tDeg system is extended to non‐repetitive sequence imaging using only one sgRNA with two aptamer insertions. Neither complex sgRNA design nor difficult plasmid construction is required, greatly reducing the technical barriers to define spatiotemporal organization and dynamics of both repetitive and non‐repetitive genomic loci in living cells, and thus demonstrating the large application potential of this genomic‐imaging system in biological research, clinical diagnosis and therapy.

Abstract Hepatic ischemia‐reperfusion injury (HIRI) is a prevalent issue during liver resection and transplantation, with currently no cure or FDA‐approved therapy. A promising drug, Cyclosporin A (CsA), ameliorates HIRI by maintaining mitochondrial homeostasis but has systemic side effects due to its low bioavailability and high dosage requirements. This study introduces a biomimetic CsA delivery system that directly targets hepatic lesions using mesenchymal stem cell (MSC) membrane‐camouflaged liposomes. These hybrid nanovesicles (NVs), leveraging MSC‐derived proteins, demonstrate efficient inflammatory chemotaxis, transendothelial migration, and drug‐loading capacity. In a HIRI mouse model, the biomimetic NVs accumulated at liver injury sites entered hepatocytes, and significantly reduced liver damage and restore function using only one‐tenth of the CsA dose typically required. Proteomic analysis verifies the protection mechanism, which includes reactive oxygen species inhibition, preservation of mitochondrial integrity, and reduced cellular apoptosis, suggesting potential for this biomimetic strategy in HIRI intervention.

Biosensing is vital for many areas like disease diagnosis, infectious disease prevention, and point-of-care monitoring. Microfluidics has been evidenced to be a powerful tool for biosensing via integrating biological detection processes into a palm-size chip. Based on the chip structure, microfluidics has two subdivision types: open microfluidics and closed microfluidics, whose operation methods would be diverse. In this review, we summarize fundamentals, liquid control methods, and applications of open and closed microfluidics separately, point out the bottlenecks, and propose potential directions of microfluidics-based biosensing.

The vibrational response amplitudes reduction is investigated for the axially moving composite cantilever rectangular (AM-CCR) laminated plate subjected to the transverse and parametric excitation by using the nonlinear energy sink (NES). Three different NES installed methods which are one NES, two NESs in series and two NESs in parallel are compared for the vibration attenuation of the AM-CCR laminated plate. For the dynamic model of the AM-CCR laminated plate with transverse and parametric excitation, using the first-order shear deformation theory, the von Karman large deformation theory, Hamilton’s principle, and the Galerkin discrete technique, the ordinary differential motion equations of the system are obtained. The pseudo frequencies and the nonlinear vibrational behaviors are depicted for the AM-CCR laminated plate. The nonlinear dynamic motion equations are established for the AM-CCR laminated plate with one NES, two NESs in series and two NESs in parallel are derived, respectively. The comparisons are given for vibrational response amplitude attenuation of the different locations in which the NES is placed on the end of the plate under the various retractable velocities. In addition, the influences of the number and the series and parallel connections of the NESs on the vibrational response reduction of the AM-CCR laminated plate are studied by the simulation method. When the AM-CCR laminated plate contracts, the scheme attached NESs in series has the optimal effect of the vibration reduction. When the AM-CCR laminated plate extends, the scheme with NESs in parallel has better effectiveness for the vibrational response reduction [ABSTRACT FROM AUTHOR]

Content of MgO in Jinchuan copper-nickel sulfide ore is high, mainly serpentine. The production quota show that the content of MgO was fluctuates greatly in concentrate and exceeds the standard seriously, and the metal grade is high in tailings and the metal loss is large under the current technological process, which seriously affects the economic benefits of the concentrator. In order to improve the production target, the existing process flow was reformed on the basis of process investigation. The production results show that the content of MgO in high grade concentrate is less than 6.8%, the nickel recovery rate is increased to more than 87%, and the copper recovery rate is increased to about 79% after technical transformation. The concentrate quality and metal recovery rate are greatly improved, which brings good economic benefits to the concentrator.

Metal–organic frameworks (MOFs), constructed by coordination between metal-containing nodes and organic linkers, are widely used in various fields due to the advantages of tunable pores, diverse functional sites, stable structure, and multi-functionality. It should be noted that MOF-based materials play a major role in glucose detection, serving as a signal transducer or functional substrate for embedding nanoparticles/enzymes. Diabetes is one of the most common and fast-growing diseases worldwide, whose main clinical manifestation is high blood sugar levels. Therefore, accurate, sensitive, and point-of-care glucose detection is necessary. This review orderly introduces general synthetic strategies of MOF-based materials (pristine MOF, nanoparticles, or enzymes-modified MOF and MOF-derived materials) and detection methods (electrochemical and optical methods) for glucose detection. Then, the review refers to the novel MOF-based glucose detection devices (flexible wearable devices and microfluidic chips), which enable non-invasive continuous glucose monitoring or low-cost microscale detection. On the basis of describing the development of glucose sensors based on MOF materials in the past five years, the review presents merits, demerits, and possible improvements of various detection methods.

Abstract Background The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein–protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. Results We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein–protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. Conclusion The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/ .

Abstract Background Dysfunction of cerebral autoregulation is one of the pathophysiological mechanisms that causes delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH). Pressure reactivity index (PRx) have been confirmed to reflect the level of cerebral autoregulation and used to derive optimal cerebral perfusion pressure (CPPopt). The goal of this study is to explore the associations between autoregulation, CPPopt, PRx, and DCI. Methods Continuous intracranial pressure (ICP), arterial blood pressure (ABP), and cerebral perfusion pressure (CPP) signals acquired from 61 aSAH patients were retrospectively analyzed. PRx was calculated and collected by Pneumatic computer system. The CPP at the lowest PRx was determined as the CPPopt. The duration of a hypoperfusion event (dHP) was defined as the cumulative time that the PRx was > 0.3 and the CPP was

The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

Background: Radio- and chemotherapy (RT/CT) resistance hampers success in combating small and non-small cell lung cancers (SCLC/NSCLC). The underlying molecular mechanisms of RT/CT resistance of LCs are multifactorial and have been understood in part hitherto. miRNAs, key regulators of mRNAs, are well-recognised oncomirs; however, their role in regulating RT response remains poorly understood.Methods: Six human NSCLC and five SCLC cell lines with different SF2 values were investigated. Using microarray we examined whether expression of miRNAs is linked to the RT resistance of NSCLCs or SCLCs. Obtained data were validated by quantitative real-time PCR. Apoptosis and senescence were analysed using siRNA transfection, western blot and flow cytometry.Results: miRNA-21, miRNA-1827, miRNA-214, miRNA-339-5p, miRNA-625, miRNA-768-3p, miRNA-523-3p, miRNA-1227, miRNA-324-5p, miRNA-423-3p, miRNA-1301 and miRNA-1249 are differentially expressed in LC cells. miRNA-214 is upregulated in RT-resistant NSCLC cells relative to radiosensitive counterparts. Considering miRNA-214 as a putative regulator of RT resistance, we demonstrate that knockdown of miRNA-214 in radioresistant NSCLCs sensitised them to RT by stimulation of senescence. Consistently, overexpression of miRNA-214 in radiosensitive NSCLCs protected against RT-induced apoptosis. Protection was mediated by p38MAPK, as downregulation of this kinase could reverse the miRNA-214 overexpression-induced resistance of NSCLC cells.Conclusion: miRNA profiling of LC revealed putative RT resistance signalling circuits, which might help in sensitisation of LC to RT. [ABSTRACT FROM AUTHOR]

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level “interactions with interactions,” such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new “unspecified interaction” glyph is added for visualizing interactions whose nature is unknown, the “insulator” glyph is deprecated in favor of a new “inert DNA spacer” glyph, and the polypeptide region glyph is recommended for showing 2A sequences.

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA–disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision–recall curve (AUPR) of 0.9379 and an area under the receiver–operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets.

Parkinson’s disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson’s disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision–recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Epigenetic modification may affect the expression of multiple inflammatory genes in lungs of patients with chronic obstructive pulmonary disease (COPD). Major epigenetic events include DNA methylation and various post-translational modifications of histones, such as histone methylation, acetylation, phosphorylation, ubiquitination, and sumoylation. Enzymes which regulate these epigenetic modifications can be activated by smoking. Both environmental and genetic factors play significant effect in development of COPD which have been reported by most references; however, little is known about the epigenetic pathways involved in the disease. Understanding the epigenetic mechanisms can help us clarify the pathogenesis of COPD and identify novel targets for developing new therapies for patients with COPD. [ABSTRACT FROM AUTHOR]

Carbonate reservoirs in the South China Sea mostly contain natural fractures with various length scales and different intensities, which causes great challenges in efficient reservoir modeling and flow simulation. Existing efforts based on dual-porosity and dual-permeability models could not reflect the characteristics of production data in certain wells. To accurately and efficiently characterize multiscale fractures, a hybrid fracture characterization method is proposed. Firstly, fractures are divided into two types according to the geometrical size and interpretation approach. Then, small-scale fractures, characterized mainly by image log interpretations, are modeled by the traditional dual-porosity/dual-permeability (DP) method. And large-scale fractures, which are characterized by seismic interpretations and dominate the flow regime, are modeled by the embedded discrete fracture method (EDFM) to achieve both accuracy and efficiency. Lastly, transmissibilities among these three types of grid mediums are calculated to generate the hybrid DP+EDFM model for flow simulation. The proposed approach is applied to a carbonate, fractured reservoir in the South China Sea. The overall procedure is fast and reliable, and water cut matches of both field and specific wells are dramatically improved. Comparing the simulation results with the conventional DP model, the proposed approach yields much more accurate predictions on rapid water breakthrough and high water cut in fractured reservoirs.

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

Compared with the two-level APF, the three-level neutral point clamped (ANPC) has more output levels, less harmonic influence, and lower equipment loss in the process of harmonic compensation. However, due to the conservatism of single-step prediction, it can only provide the reliability at k+1 time, and it does not provide the relevant information for the next time optimal. Moreover, the control of threelevel APF needs to consider multiple targets at the same time, which will cause certain tracking error. In this paper, a multi-step prediction method is proposed. The optimal and suboptimal switching states at k+1 are obtained by cycling. The optimal switching states in two control cycles can be obtained by substituting the optimal switching states at k+2. The method improves the reliability of the control process, and the simulation results show that the harmonic content of the power grid is low, which meets the relevant requirements.

2-(3-Acetyl-2,2-dimethylcyclobutyl)acetic acid (pinonic acid) was synthesized using α-pinene as raw material and potassium permanganate as oxidant. This compound reacted with substituted aniline to produce eight kinds of derivatives with cyclobutane moiety. The yields of the cyclobutane analogues ranged from 24.9 to 78.2 %.

A class of small-deviation theorems for the relative entropy densities of arbitrary random field on the generalized Bethe tree are discussed by comparing the arbitrary measure with the Markov measure on the generalized Bethe tree. As corollaries, some Shannon-Mcmillan theorems for the arbitrary random field on the generalized Bethe tree, Markov chain field on the generalized Bethe tree are obtained.

To study dosimetric effects of leaf positioning errors (LPEs) of the Halcyon(2.0) dual-layer MLC on the long-course chemoradiotherapy (LCCRT) with 45∼50.4 Gy in 25∼28 fractions for rectal cancer. Nine Halcyon(2.0)-based LCCRT plans of rectal cancer were retrospectively involved. Four types of LPEs were introduced: (1) Uniformly distributed dual-layer random LPEs (Dual-R); (2) Proximal-layer systemic LPEs (P-S); (3) Distal-layer systemic LPEs (D-S); (4) Dual-layer systemic LPEs (Dual-S). The sensitivities of D 98% , D 2% and the Equivalent Uniform Dose (EUD) of PTV to various LPEs were investigated as well as varying ranges of EUDs of OARs. The sensitivities of D 98% and EUD of PTV to Dual-R was -0.65%/mm and -0.38%/mm; the sensitivities of both indices to the P-S and D-S were similar to each other, ranging from 1.92%/mm to 2.87%/mm; both indices were more sensitive to the Dual-S and values were 4.97%/mm and 3.84%/mm respectively. The EUD changes of the bladder, and left and right femoral heads were from -13.23% to 14.82%. Single-side systemic LPEs lower than 0.7 mm are acceptable for Halcyon(2.0)-based LCCRT, while dual-layer systemic LPEs lower than 0.4 mm are acceptable, considering relative changes of 2% for D 98% of PTV as threshold., Competing Interests: Conflicts of Interest The author declares no conflicts of interest., (Copyright © 2024 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.)

Regulatory B (Breg) cells is a type of immune cell that exhibit immunosuppressive behavior within the tumor microenvironment. However, the differentiation and regulatory mechanisms of these Breg cells remain unexplored. Single-cell transcriptome sequencing analysis of human nasopharyngeal carcinoma (NPC) revealed a significant enrichment of B cell subset characterized by high expression of EGR1 and EGR3 in the tumor microenvironment. Notably, in the hypoxic microenvironment, these B cells induce MAPK pathway activation, subsequently triggering the activation of transcription factors EGR1 and EGR3, which further modulate the expression of immunosuppressive factors like TGFB1 and IL10. In transplant experiments using primary B cells induced under hypoxia and co-transplanted with cancer cells, a significant increase in tumor growth was observed. Mechanism experiments demonstrated that EGR1 hi and EGR3 + B cells further activate the maturation and immunosuppressive function of Treg cells through the secretion of IL16 and TNF-α. Hence, this study identifies the key transcription factors EGR1 and EGR3 as essential regulators and elucidates the differentiation of Breg cells under hypoxic conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Crop rotation is an important agricultural practice that often affects the metabolic processes of soil microorganisms through the composition and combination of crops, thereby altering nutrient cycling and supply to the soil. Although the benefits of crop rotation have been extensively discussed, the effects and mechanisms of different crop combinations on the soil microbial community structure in specific environments still need to be analyzed in detail., Materials and Methods: In this study, six crop rotation systems were selected, for which the spring crops were mainly tobacco or gramineous crops: AT (asparagus lettuce and tobacco rotation), BT (broad bean and tobacco rotation), OT (oilseed rape and tobacco rotation), AM (asparagus lettuce and maize rotation), BM (broad bean and maize rotation), and OR (oilseed rape and rice rotation). All crops had been cultivated for > 10 years. Soil samples were collected when the rotation was completed in spring, after which the soil properties, composition, and functions of bacterial and fungal communities were analyzed., Results: The results indicate that spring cultivated crops play a more dominant role in the crop rotation systems than do autumn cultivated crops. Crop rotation systems with the same spring crops have similar soil properties and microbial community compositions. pH and AK are the most important factors driving microbial community changes, and bacteria are more sensitive to environmental responses than fungi. Rotation using tobacco systems led to soil acidification and a decrease in microbial diversity, while the number of biomarkers and taxonomic indicator species differed between rotation patterns. Symbiotic network analysis revealed that the network complexity of OT and BM was the highest, and that the network density of tobacco systems was lower than that of gramineous systems., Conclusions: Different crop rotation combinations influence both soil microbial communities and soil nutrient conditions. The spring crops in the crop rotation systems had stronger dominating effects, and the soil bacteria were more sensitive than the fungi were to environmental changes. The tobacco rotation system can cause soil acidification and thereby affect soil sustainability, while the complexity of soil microbial networks is lower than that of gramineous systems. These results provide a reference for future sustainable applications of rotation crop systems., (© 2024. The Author(s).)

Chronic airway inflammation induced by cigarette smoke (CS) plays an essential role in the pathogenesis of chronic obstructive pulmonary disease (COPD). MALAT1 is involved in a variety of inflammatory disorders. However, studies focusing on the interaction between MALAT1 and CS-induced airway inflammation remain unknown. The present study investigated the effects and mechanisms of MALAT1 in CS-induced airway inflammation in the pathogenesis of COPD. RT-qPCR was employed to determine the mRNA levels of MALAT1, miR-30a-5p and inflammatory cytokines. Protein concentrations of IL-1β and IL-6 in cell culture supernatant and mouse bronchoalveolar lavage fluid (BALF) were assessed by ELISA assay kits. Dual-luciferase reporter assay was conducted to verify the interaction between MALAT1 and miR-30a-5p. The protein expression of JNK and p-JNK was determined by western blot (WB). MALAT1 was highly expressed in cigarette smoke extract (CSE)-treated human bronchial epithelial cells (HBECs) and COPD mice lung tissues. Knockdown of MALAT1 significantly alleviate CS-induced inflammatory response. MALAT1 directly interacted with miR-30a-5p and knockdown of miR-30a-5p significantly inhibit the protective effects of MALAT1 silencing after CS exposure. Additionally, our results showed that miR-30a-5p could regulate inflammation via modulating the activation of JNK signaling pathway. Moreover, our results demonstrated MALAT1 could activate JNK signaling pathway by sponging miR-30a-5p. Our results demonstrated MALAT1 promotes CS-induced airway inflammation by inhibiting the activation of JNK signaling pathway via sponging miR-30a-5p., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Pinus armandii seed kernel is a nutrient-rich and widely consumed nut whose yield and quality are affected by, among other things, harvesting time and climatic conditions, which reduce economic benefits. To investigate the optimal harvesting period of P. armandii seed kernels, this study determined the nutrient composition and seed kernel morphology and analyzed the gene expression and metabolic differences of P. armandii seed kernels during the harvesting period by transcriptomics and metabolomics. The results revealed that during the maturation of P. armandii seed kernels, there was a significant increase in the width, thickness, and weight of the seed kernels, as well as a significant accumulation of sucrose, soluble sugars, proteins, starch, flavonoids, and polyphenols and a significant decrease in lipid content. In addition, transcriptomic and metabolomic analyses of P. armandii seed kernels during the harvesting period screened and identified 103 differential metabolites (DEMs) and 8899 differential genes (DEGs). Analysis of these DEMs and DEGs revealed that P. armandii seed kernel harvesting exhibited gene-metabolite differences in sugar- and lipid-related pathways. Among them, starch and sucrose metabolism, glycolysis, and gluconeogenesis were associated with the synthesis and catabolism of sugars, whereas fatty acid degradation, glyoxylate and dicarboxylic acid metabolism, and glycerophospholipid metabolism were associated with the synthesis and catabolism of lipids. Therefore, the present study hypothesized that these differences in genes and metabolites exhibited during the harvesting period of P. armandii seed kernels might be related to the accumulation and transformation of sugars and lipids. This study may provide a theoretical basis for determining the optimal harvesting time of P. armandii seed kernels, changes in the molecular mechanisms of nutrient accumulation, and quality directed breeding.