Publications by authors named "Chao Shen"

337 Publications

Human nuclear receptors (NRs) genes have prognostic significance in hepatocellular carcinoma patients.

World J Surg Oncol 2021 Apr 30;19(1):137. Epub 2021 Apr 30.

Department of Hepatobiliary Surgery, ZiBo Central Hospital, No. 54, Gongqingtuanxi Road, Zibo, Shandong, 255036, People's Republic of China.

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world.

Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from The Cancer Genome Atlas (TCGA) database. The consensus clustering analysis with the mRNA levels of 48 nuclear receptors (NRs) was performed by the "ConsensusClusterPlus." The univariate Cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs. Then multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors was used to predict the long-term survival of HCC patients.

Results: The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG, and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and tumor node metastasis (TNM) stage and could better predict the long-term survival for 3- and 5-year of HCC patients.

Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.
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http://dx.doi.org/10.1186/s12957-021-02246-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8091722PMC
April 2021

Gossypium tomentosum genome and interspecific ultra-dense genetic maps reveal genomic structures, recombination landscape and flowering depression in cotton.

Genomics 2021 Apr 26;113(4):1999-2009. Epub 2021 Apr 26.

National Key Laboratory of Crop Genetic Improvement, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China. Electronic address:

The high-quality reference-grade genome for Gossupium tomentosum can greatly promote the progress in biological research and introgression breeding for the mainly cultivated species, G. hirsutum. Here, we report a high-quality genome assembly for G. tomentosum by integrating PacBio and Hi-C technologies. Comparative genomic analysis revealed a large number of genetic variations. Two re-sequencing-based ultra-dense genetic maps were constructed which comprised 4,047,199 and 6,009,681 SNPs, 4120 and 4599 bins and covering 4126.36 cM and 4966.72 cM in the EMF (F from G. hirsutum × G. tomentosum) and GHF (F from G. hirsutum × G. barbadense). The EMF exhibited lower recombination rate at the whole-genome level as compared with GHF. We mapped 22 and 33 QTL associated with crossover frequency and predicted Gh_MRE11 and Gh_FIGL1 as the candidate genes governing crossover in the EMF and GHF, respectively. We identified 13 significant QTL that regulate the floral transition, and revealed that Gh_AGL18 was associated with the floral transition. Therefore, our study provides a valuable genomic resource to support a better understanding of cotton interspecific cross and recombination landscape for genetic improvement and breeding in cotton.
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http://dx.doi.org/10.1016/j.ygeno.2021.04.036DOI Listing
April 2021

Genome-wide association mapping for agronomic traits in an 8-way Upland cotton MAGIC population by SLAF-seq.

Theor Appl Genet 2021 Apr 28. Epub 2021 Apr 28.

National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences & Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China.

Key Message: One sub-MAGIC population was genotyped using SLAF-seq, and QTLs and candidate genes for agronomic traits were identified in Upland cotton. The agronomic traits of Upland cotton have serious impacts on cotton production, as well as economic benefits. To discover the genetic basis of important agronomic traits in Upland cotton, a subset MAGIC (multi-parent advanced generation inter-cross) population containing 372 lines (SMLs) was selected from an 8-way MAGIC population with 960 lines. The 372 lines and 8 parents were phenotyped in six environments and deeply genotyped by SLAF-seq with 60,495 polymorphic SNPs. The genetic diversity indexes of all SNPs were 0.324 and 0.362 for the parents and MAGIC lines, respectively. The LD decay distance of the SMLs was 600 kb (r = 0.1). Genome-wide association mapping was performed using 60,495 SNPs and the phenotypic data of the SMLs, and 177 SNPs were identified to be significantly associated with 9 stable agronomic traits in multiple environments. The identified SNPs were divided into 117 QTLs (quantitative trait loci) by LD decay distance, explaining 5.44% to 31.64% of the phenotypic variation. Among the 117 QTLs, 3 QTLs were stable in multiple environments, and 11 QTL regions were proven to have pleiotropism associated with multiple traits. Within QTL regions, 154 genes were preferentially expressed in correlated tissues, and 8 genes with known functions were identified as priori candidate genes. Two genes, GhACT1 and GhGASL3, reported to have clear functions, were, respectively, located in qFE-A05-4 and qFE-D04-3, two stable QTLs for FE. This study revealed the genetic basis of important agronomic traits of Upland cotton, and the results will facilitate molecular breeding in cotton.
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http://dx.doi.org/10.1007/s00122-021-03835-wDOI Listing
April 2021

Genetic Variants in and Are Associated With the Risk of HCV Infection Among Chinese High-Risk Population.

Front Genet 2021 25;12:630310. Epub 2021 Mar 25.

Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Background: The tumor necrosis factor superfamily () and TNF receptor superfamily () play important roles in the immune responses to infections. The aim of this study was to determine the impact of single nucleotide polymorphisms (SNPs) of several genes on the risk of hepatitis C virus (HCV) infection in the Chinese high-risk population.

Methods: The -rs1234313, -rs7514229, -rs3181366, -rs2295800, -rs2298209, and -rs2230625 SNPs were genotyped in 2309 uninfected controls, 597 subjects with spontaneous HCV clearance and 784 patients with persistent HCV infection using the TaqMan-MGB assay. The putative functions of the positive SNPs were determined using online bioinformatics tools.

Results: After adjusting for gender, age, high-risk population, alanine transaminase (ALT), aspartate aminotransferase (AST), -rs12979860 and rs8099917 genotypes, the non-conditional logistic regression showed that rs7514229-T, rs3181366-T, and rs2295800-C were associated with an increased risk of HCV infection (all < 0.05). Combined analysis of rs7514229-T and rs3181366-T risk alleles showed that the subjects carrying 2-4 risk alleles were more susceptible to HCV infection compared with those lacking any risk allele (all < 0.001). Furthermore, the risk of HCV infection increased with the number of risk alleles ( < 0.001). analysis showed that rs7514229, rs3181366, and rs2295800 polymorphisms may affect the transcription of mRNA by regulating miRNA binding, TF binding, and promoter activation, respectively, which may have biological consequences.

Conclusion: -rs7514229, -rs3181366, and -rs2295800 are associated with increased risk of HCV infection in the Chinese high-risk population.
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http://dx.doi.org/10.3389/fgene.2021.630310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027328PMC
March 2021

Primary squamous cell carcinoma of the thyroid: a case report.

J Int Med Res 2021 Apr;49(4):3000605211004702

Department of Endocrinology, Peking University International Hospital, Beijing, China.

Primary squamous cell carcinoma of the thyroid (PSCCT) is a rare and rapidly progressive malignancy that carries a poor prognosis. PSCCT is easily misdiagnosed as acute thyroiditis or as another thyroid malignancy. We have reported a 76-year-old woman who presented with progressive neck pain for 1 month. Thyroid function tests revealed subclinical thyrotoxicosis. Ultrasound disclosed a solid nodule with calcification in the right thyroid lobe. Laboratory findings included neutrophilic leukocytosis and an elevated erythrocyte sedimentation rate. The patient's condition was diagnosed as subacute thyroiditis, and she was treated with cefixime and ibuprofen. However, her treatment response was poor. She was then treated with oral prednisone. Her neck pain gradually resolved. The patient subsequently developed dysphagia, choking, dyspnea, and dysphonia with an insidious onset. Further examinations including computed tomography and painless gastroscopy revealed that the volume of the thyroid gland had increased significantly, extending to the anterior superior mediastinum. The trachea and esophagus were stenotic because of external compression. Partial thyroidectomy and tracheotomy were performed under extracorporeal membrane oxygenation. The diagnosis of PSCCT was established via histopathology and immunohistochemistry.
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http://dx.doi.org/10.1177/03000605211004702DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8040576PMC
April 2021

Identification of active molecules against Mycobacterium tuberculosis through machine learning.

Brief Bioinform 2021 Apr 5. Epub 2021 Apr 5.

College of Pharmaceutical Sciences at Zhejiang University, China.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and it has been one of the top 10 causes of death globally. Drug-resistant tuberculosis (XDR-TB), extensively resistant to the commonly used first-line drugs, has emerged as a major challenge to TB treatment. Hence, it is quite necessary to discover novel drug candidates for TB treatment. In this study, based on different types of molecular representations, four machine learning (ML) algorithms, including support vector machine, random forest (RF), extreme gradient boosting (XGBoost) and deep neural networks (DNN), were used to develop classification models to distinguish Mtb inhibitors from noninhibitors. The results demonstrate that the XGBoost model exhibits the best prediction performance. Then, two consensus strategies were employed to integrate the predictions from multiple models. The evaluation results illustrate that the consensus model by stacking the RF, XGBoost and DNN predictions offers the best predictions with area under the receiver operating characteristic curve of 0.842 and 0.942 for the 10-fold cross-validated training set and external test set, respectively. Besides, the association between the important descriptors and the bioactivities of molecules was interpreted by using the Shapley additive explanations method. Finally, an online webserver called ChemTB (http://cadd.zju.edu.cn/chemtb/) was developed, and it offers a freely available computational tool to detect potential Mtb inhibitors.
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http://dx.doi.org/10.1093/bib/bbab068DOI Listing
April 2021

Sustainable Route for Synthesizing Aluminosilicate EU-1 Zeolite.

Molecules 2021 Mar 8;26(5). Epub 2021 Mar 8.

College of Biological, Chemical Science and Engineering, Jiaxing University, Jiaxing 314001, China.

Developing sustainable routes for the synthesis of zeolites is still a vital and challenging task in zeolite scientific community. One of the typical examples is sustainable synthesis of aluminosilicate EU-1 zeolite, which is not very efficient and environmental-unfriendly under hydrothermal condition due to the use of a large amount of water as solvent. Herein, we report a sustainable synthesis route for aluminosilicate EU-1 zeolite without the use of solvent for the first time. The physicochemical properties of the obtained EU-1 zeolite are characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetry-differential thermal analysis (TG-DTA), N sorption, inductively coupled plasma (ICP) analysis, and solid nuclear magnetic resonance (NMR), which show the product has high crystallinity, uniform morphology, large BET surface area, and four-coordinated aluminum species. Moreover, the impact of synthesis conditions is investigated in detail. The sustainable synthesis of aluminosilicate EU-1 zeolite under solvent-free.
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http://dx.doi.org/10.3390/molecules26051462DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962662PMC
March 2021

Discovery of novel antagonists targeting the DNA binding domain of androgen receptor by integrated docking-based virtual screening and bioassays.

Acta Pharmacol Sin 2021 Mar 25. Epub 2021 Mar 25.

Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.

Androgen receptor (AR), a ligand-activated transcription factor, is a master regulator in the development and progress of prostate cancer (PCa). A major challenge for the clinically used AR antagonists is the rapid emergence of resistance induced by the mutations at AR ligand binding domain (LBD), and therefore the discovery of novel anti-AR therapeutics that can combat mutation-induced resistance is quite demanding. Therein, blocking the interaction between AR and DNA represents an innovative strategy. However, the hits confirmed targeting on it so far are all structurally based on a sole chemical scaffold. In this study, an integrated docking-based virtual screening (VS) strategy based on the crystal structure of the DNA binding domain (DBD) of AR was conducted to search for novel AR antagonists with new scaffolds and 2-(2-butyl-1,3-dioxoisoindoline-5-carboxamido)-4,5-dimethoxybenzoicacid (Cpd39) was identified as a potential hit, which was competent to block the binding of AR DBD to DNA and showed decent potency against AR transcriptional activity. Furthermore, Cpd39 was safe and capable of effectively inhibiting the proliferation of PCa cell lines (i.e., LNCaP, PC3, DU145, and 22RV1) and reducing the expression of the genes regulated by not only the full-length AR but also the splice variant AR-V7. The novel AR DBD-ARE blocker Cpd39 could serve as a starting point for the development of new therapeutics for castration-resistant PCa.
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http://dx.doi.org/10.1038/s41401-021-00632-5DOI Listing
March 2021

A novel model for predicting fatty liver disease by means of an artificial neural network.

Gastroenterol Rep (Oxf) 2021 Jan 24;9(1):31-37. Epub 2020 Aug 24.

Department of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.

Background: The artificial neural network (ANN) emerged recently as a potent diagnostic tool, especially for complicated systemic diseases. This study aimed to establish a diagnostic model for the recognition of fatty liver disease (FLD) by virtue of the ANN.

Methods: A total of 7,396 pairs of gender- and age-matched subjects who underwent health check-ups at the First Affiliated Hospital, College of Medicine, Zhejiang University (Hangzhou, China) were enrolled to establish the ANN model. Indices available in health check-up reports were utilized as potential input variables. The performance of our model was evaluated through a receiver-operating characteristic (ROC) curve analysis. Other outcome measures included diagnostic accuracy, sensitivity, specificity, Cohen's k coefficient, Brier score, and Hosmer-Lemeshow test. The Fatty Liver Index (FLI) and the Hepatic Steatosis Index (HSI), retrained using our training-group data with its original designated input variables, were used as comparisons in the capability of FLD diagnosis.

Results: Eight variables (age, gender, body mass index, alanine aminotransferase, aspartate aminotransferase, uric acid, total triglyceride, and fasting plasma glucose) were eventually adopted as input nodes of the ANN model. By applying a cut-off point of 0.51, the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908 [95% confidence interval (CI), 0.901-0.915]-significantly higher (<0.05) than that of the FLI model (0.881, 95% CI, 0.872-0.891) and that of the HSI model (0.885; 95% CI, 0.877-0.893). Our ANN model exhibited higher diagnostic accuracy, better concordance with ultrasonography results, and superior capability of calibration than the FLI model and the HSI model.

Conclusions: Our ANN system showed good capability in the diagnosis of FLD. It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.
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http://dx.doi.org/10.1093/gastro/goaa035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962739PMC
January 2021

High-Energy Aqueous Sodium-Ion Batteries.

Angew Chem Int Ed Engl 2021 May 22;60(21):11943-11948. Epub 2021 Apr 22.

Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20742, USA.

Water-in-salt electrolytes (WISE) have largely widened the electrochemical stability window (ESW) of aqueous electrolytes by formation of passivating solid electrolyte interphase (SEI) on anode and also absorption of the hydrophobic anion-rich double layer on cathode. However, the cathodic limiting potential of WISE is still too high for most high-capacity anodes in aqueous sodium-ion batteries (ASIBs), and the cost of WISE is also too high for practical application. Herein, a low-cost 19 m (m: mol kg ) bi-salts WISE with a wide ESW of 2.8 V was designed, where the low-cost 17 m NaClO extends the anodic limiting potential to 4.4 V, while the fluorine-containing salt (2 m NaOTF) extends the cathodic limiting potential to 1.6 V by forming the NaF-Na O-NaOH SEI on anode. The 19 m NaClO -NaOTF-H O electrolyte enables a 1.75 V Na V (PO ) ∥Na V (PO ) full cell to deliver an appreciable energy density of 70 Wh kg at 1 C with a capacity retention of 87.5 % after 100 cycles.
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http://dx.doi.org/10.1002/anie.202017167DOI Listing
May 2021

Changes and significance of plasma fibrinogen gamma-chain concentration in preeclampsia patients.

J Clin Lab Anal 2021 Apr 8;35(4):e23704. Epub 2021 Mar 8.

Laboratory Department, Jiujiang University Clinical Medical College, Jiujiang University Hospital, Jiujiang City, China.

Objective: To investigate the plasma fibrinogen gamma-chain concentration in preeclampsia patients and explore its value in preeclampsia prediction and auxiliary diagnosis.

Methods: Follow-up of pregnant women who regularly attended perinatal care at two hospitals in China was performed, and clinical data and plasma samples were collected at each examination until delivery. The gamma-chain concentration was detected by Western blotting, and Quantity One Software was used for gamma-chain grayscale value measurements.

Results: Forty-two patients with preeclampsia and 42 control patients completed the follow-up. In the control group, the gamma-chain concentration at 32 weeks of gestation was higher than that at 20 weeks of gestation, but the difference was not statistically significant (p > 0.05). In the experimental group, the gamma-chain concentration at preeclampsia diagnosis was significantly higher than that at 20 weeks of gestation (p < 0.05). Compared with the control group, the gamma-chain concentration was higher at 20 weeks of gestation in the experimental group, but the difference was not statistically significant. However, at 32 weeks of gestation and at the time of diagnosis, the gamma-chain concentration in the experimental group was significantly higher than that in the control group (p < 0.05). At 32 weeks of gestation and at the time of diagnosis, the AUCs from ROC curve analysis of plasma fibrinogen gamma-chain concentrations were 0.64 and 0.71, respectively.

Conclusion: Plasma fibrinogen synthesis and degradation were disrupted in preeclampsia patients before and after diagnosis, and gamma-chain concentration was significantly increased. Plasma fibrinogen gamma chain may be of some value in preeclampsia prediction and auxiliary diagnosis.
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http://dx.doi.org/10.1002/jcla.23704DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059740PMC
April 2021

Design and Implementation of a Real-Time Multi-Beam Sonar System Based on FPGA and DSP.

Sensors (Basel) 2021 Feb 18;21(4). Epub 2021 Feb 18.

Key Laboratory of Acoustics Research, China Jiliang University, Hangzhou 310018, China.

Aiming at addressing the contradiction between the high-speed real-time positioning and multi-channel signal processing in multi-beam sonar systems, in this work we present a real-time multi-beam sonar system based on a Field Programmable Gate Array (FPGA) and Digital Signal Processing (DSP) from two perspectives, i.e., hardware implementation and software optimization. In terms of hardware, an efficient high-voltage pulse transmitting module and a multi-channel data acquisition module with time versus gain (TVG) compensation with characteristics such as low noise and high phase amplitude consistency, are proposed. In terms of algorithms, we study three beamforming methods, namely delay-and-sum (D&S), direct-method (DM) and Chirp Zeta Transform (CZT). We compare the computational efficiency of DM and CZT in the digital domain. In terms of software, according to the transmission bandwidth of the Gigabit Ethernet and a serial rapid IO (SRIO) interface, the data transmission paths of the acquired data and the beam pattern between the FPGA, the DSP, and a personal computer (PC) are planned. A master-slave multi-core pipelined signal processing architecture is designed based on DSP, which enhances the data throughput of the signal processor by seven times as compared with that of the single-core operation. The experimental results reveal that the sound source level of the transmitting module is around 190.25 dB, the transmitting beam width is 64° × 64°, the background noise of the acquisition module is less than 4 μVrms, the amplitude consistency error of each channel is less than -6.55 dB, and the phase consistency error is less than 0.2°. It is noteworthy that the beam number of the sonar system is 90 × 90, the scanning angle interval is 0.33°, the working distance ranges from 5 m to 40 m, and the maximum distance resolution is 0.384 m. In the positioning experiment performed in this work; the 3-D real-time position of the baffle placed in the detection sector is realized. Please note that the maximum deviation of azimuth is 2°, the maximum deviation of elevation is 2.3°, and the maximum distance deviation is 0.379 m.
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http://dx.doi.org/10.3390/s21041425DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7922857PMC
February 2021

PdGNC confers drought tolerance by mediating stomatal closure resulting from NO and H O production via the direct regulation of PdHXK1 expression in Populus.

New Phytol 2021 06 26;230(5):1868-1882. Epub 2021 Mar 26.

Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.

Drought is one of the primary abiotic stresses, seriously implicating plant growth and productivity. Stomata play a crucial role in regulating drought tolerance. However, the molecular mechanism on stomatal movement-mediated drought tolerance remains unclear. Using genetic, molecular and biochemical techniques, we identified that the PdGNC directly activating the promoter of PdHXK1 by binding the GATC element, a hexokinase (HXK) synthesis key gene. Here, PdGNC, a member of the GATA transcription factor family, was greatly induced by abscisic acid and dehydration. Overexpressing PdGNC in poplar (Populus clone 717) resulted in reduced stomatal aperture with greater water-use efficiency and increased water deficit tolerance. By contrast, CRISPR/Cas9-mediated poplar mutant gnc exhibited increased stomatal aperture and water loss with reducing drought resistance. PdGNC activates PdHXK1 (a hexokinase synthesis key gene), resulting in a remarkable increase in hexokinase activity in poplars subjected to water deficit. Furthermore, hexokinase promoted nitric oxide (NO) and hydrogen peroxide (H O ) production in guard cells, which ultimately reduced stomatal aperture and increased drought resistance. Together, PdGNC confers drought stress tolerance by reducing stomatal aperture caused by NO and H O production via the direct regulation of PdHXK1 expression in poplars.
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http://dx.doi.org/10.1111/nph.17301DOI Listing
June 2021

Coinfection with influenza A virus enhances SARS-CoV-2 infectivity.

Cell Res 2021 04 18;31(4):395-403. Epub 2021 Feb 18.

State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, Hubei, 430072, China.

The upcoming flu season in the Northern Hemisphere merging with the current COVID-19 pandemic raises a potentially severe threat to public health. Through experimental coinfection with influenza A virus (IAV) and either pseudotyped or live SARS-CoV-2 virus, we found that IAV preinfection significantly promoted the infectivity of SARS-CoV-2 in a broad range of cell types. Remarkably, in vivo, increased SARS-CoV-2 viral load and more severe lung damage were observed in mice coinfected with IAV. Moreover, such enhancement of SARS-CoV-2 infectivity was not observed with several other respiratory viruses, likely due to a unique feature of IAV to elevate ACE2 expression. This study illustrates that IAV has a unique ability to aggravate SARS-CoV-2 infection, and thus, prevention of IAV infection is of great significance during the COVID-19 pandemic.
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http://dx.doi.org/10.1038/s41422-021-00473-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890106PMC
April 2021

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.

J Cheminform 2021 Feb 17;13(1):12. Epub 2021 Feb 17.

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.

Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.
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http://dx.doi.org/10.1186/s13321-020-00479-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888189PMC
February 2021

ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions.

J Cheminform 2021 Feb 4;13(1). Epub 2021 Feb 4.

Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.

Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein-ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.
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http://dx.doi.org/10.1186/s13321-021-00486-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860246PMC
February 2021

Analysis and review of trichomes in plants.

BMC Plant Biol 2021 Feb 1;21(1):70. Epub 2021 Feb 1.

Key laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University, Guiyang, Guizhou, People's Republic of China.

Background: Trichomes play a key role in the development of plants and exist in a wide variety of species.

Results: In this paper, it was reviewed that the structure and morphology characteristics of trichomes, alongside the biological functions and classical regulatory mechanisms of trichome development in plants. The environment factors, hormones, transcription factor, non-coding RNA, etc., play important roles in regulating the initialization, branching, growth, and development of trichomes. In addition, it was further investigated the atypical regulation mechanism in a non-model plant, found that regulating the growth and development of tea (Camellia sinensis) trichome is mainly affected by hormones and the novel regulation factors.

Conclusions: This review further displayed the complex and differential regulatory networks in trichome initiation and development, provided a reference for basic and applied research on trichomes in plants.
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http://dx.doi.org/10.1186/s12870-021-02840-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852143PMC
February 2021

Oxidative Sulfonylation of Hydrazones Enabled by Synergistic Copper/Silver Catalysis.

J Org Chem 2021 Mar 22;86(5):3706-3720. Epub 2021 Jan 22.

Department of Chemistry and the N.1 Institute for Health, National University of Singapore, Singapore 117543, Singapore.

A copper/silver-cocatalyzed protocol for oxidative sulfonylation of hydrazones is demonstrated. A wide range of β-ketosulfones and -acylsulfonamides are directly synthesized in moderate to good yields. Our work provides a viable method for scalable preparation of β-ketosulfone derivatives that have found wide applications in the pharmaceutical industry.
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http://dx.doi.org/10.1021/acs.joc.0c02249DOI Listing
March 2021

R-2-hydroxyglutarate attenuates aerobic glycolysis in leukemia by targeting the FTO/mA/PFKP/LDHB axis.

Mol Cell 2021 03;81(5):922-939.e9

Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA 91016, USA; City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA 91010, USA; Gehr Family Center for Leukemia Research, City of Hope, Duarte, CA 91010, USA. Electronic address:

R-2-hydroxyglutarate (R-2HG), a metabolite produced by mutant isocitrate dehydrogenases (IDHs), was recently reported to exhibit anti-tumor activity. However, its effect on cancer metabolism remains largely elusive. Here we show that R-2HG effectively attenuates aerobic glycolysis, a hallmark of cancer metabolism, in (R-2HG-sensitive) leukemia cells. Mechanistically, R-2HG abrogates fat-mass- and obesity-associated protein (FTO)/N-methyladenosine (mA)/YTH N-methyladenosine RNA binding protein 2 (YTHDF2)-mediated post-transcriptional upregulation of phosphofructokinase platelet (PFKP) and lactate dehydrogenase B (LDHB) (two critical glycolytic genes) expression and thereby suppresses aerobic glycolysis. Knockdown of FTO, PFKP, or LDHB recapitulates R-2HG-induced glycolytic inhibition in (R-2HG-sensitive) leukemia cells, but not in normal CD34 hematopoietic stem/progenitor cells, and inhibits leukemogenesis in vivo; conversely, their overexpression reverses R-2HG-induced effects. R-2HG also suppresses glycolysis and downregulates FTO/PFKP/LDHB expression in human primary IDH-wild-type acute myeloid leukemia (AML) cells, demonstrating the clinical relevance. Collectively, our study reveals previously unrecognized effects of R-2HG and RNA modification on aerobic glycolysis in leukemia, highlighting the therapeutic potential of targeting cancer epitranscriptomics and metabolism.
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http://dx.doi.org/10.1016/j.molcel.2020.12.026DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935770PMC
March 2021

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.

J Cheminform 2020 Mar 5;12(1):16. Epub 2020 Mar 5.

Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug-drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug-drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than non-inhibitors. We then developed a series of quantitative structure-activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.
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http://dx.doi.org/10.1186/s13321-020-00421-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059329PMC
March 2020

Efficacy and Safety of Glecaprevir/Pibrentasvir in HCV Patients With Previous Direct-Acting Antiviral Therapy Failures: A Meta-Analysis.

Front Med (Lausanne) 2020 3;7:592472. Epub 2020 Dec 3.

Department of Infectious Diseases, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Since a greater number of hepatitis C virus (HCV) patients have access to direct-acting antiviral (DAA) based therapies, the number of patients not properly responding to prior DAA regimens is increasing. The objective of this comprehensive analysis was to assess the efficacy and safety of glecaprevir/pibrentasvir (GLE/PIB) in HCV patients who experienced previous DAA therapy failures. Bibliographic databases were systematically searched for relevant articles published by November 2020. The main endpoints were sustained viral response after 12 weeks (SVR12), adverse events (AEs; any grade) and severe adverse events (SAEs). Publication bias assessment was performed using funnel plots and the Egger's test. Fourteen studies consisting of a total of 1,294 subjects were included in this study and the pooled estimate of SVR12, AEs and SAEs rates were 96.8% (95%CI: 95.1-98.2), 47.1% (95%CI: 26.0-69.3), and 1.8% (95%CI: 0.7-3.4), respectively. Subgroup analysis showed that pooled SVR12 rates were 97.9% (95%CI: 96.7-98.9) for Japan and 91.1% (95%CI: 87.3-94.3) for the United States; 95.8% (95%CI: 93.9-97.4) for genotype (GT)1 and 100.0% (95%CI: 99.6-100.0) for GT2; 95.3% (95%CI: 92.4-97.2) for cirrhosis and 96.3% (95%CI: 94.2-97.7) for non-cirrhosis cases. There was no publication bias included this study. This comprehensive analysis revealed that GLE/PIB is an effective and secure retreatment option for patients who did not optimally respond to DAA treatment, especially the Asian population with GT1-2.
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http://dx.doi.org/10.3389/fmed.2020.592472DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793883PMC
December 2020

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Brief Bioinform 2021 Jan 8. Epub 2021 Jan 8.

Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.
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http://dx.doi.org/10.1093/bib/bbaa410DOI Listing
January 2021

Screening of potential oestrogen receptor α agonists in pesticides via in silico, in vitro and in vivo methods.

Environ Pollut 2021 Feb 10;270:116015. Epub 2020 Nov 10.

State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China. Electronic address:

In modern agricultural management, the use of pesticides is indispensable. Due to their massive use worldwide, pesticides represent a latent risk to both humans and the environment. In the present study, 1056 frequently used pesticides were screened for oestrogen receptor (ER) agonistic activity by using in silico methods. We found that 72 and 47 pesticides potentially have ER agonistic activity by the machine learning methods random forest (RF) and deep neural network (DNN), respectively. Among endocrine-disrupting chemicals (EDCs), 14 have been reported as EDCs or ER agonists by previous studies. We selected 3 reported and 7 previously unreported pesticides from 76 potential ER agonists to further assess ERα agonistic activity. All 10 selected pesticides exhibited ERα agonistic activity in human cells or zebrafish. In the dual-luciferase reporter gene assays, six pesticides exhibited ERα agonistic activity. Additionally, nine pesticides could induce mRNA expression of the pS2 and NRF1 genes in MCF-7 cells, and seven pesticides could induce mRNA expression of the vtg1 and vtg2 genes in zebrafish. Importantly, the remaining 48 out of 76 potential ER agonists, none of which have previously been reported to have endocrine-disrupting effects or oestrogenic activity, should be of great concern. Our screening results can inform environmental protection goals and play an important role in environmental protection and early warnings to human health.
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http://dx.doi.org/10.1016/j.envpol.2020.116015DOI Listing
February 2021

A High-Performance Lithium Metal Battery with Ion-Selective Nanofluidic Transport in a Conjugated Microporous Polymer Protective Layer.

Adv Mater 2021 Feb 16;33(5):e2006323. Epub 2020 Dec 16.

Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore.

Lithium metal is the "holy grail" of anodes, capable of unlocking the full potential of cathodes in next-generation batteries. However, the use of pure lithium anodes faces several challenges in terms of safety, cycle life, and rate capability. Herein, a solution-processable conjugated microporous thermosetting polymer (CMP) is developed. The CMP can be further converted into a large-scale membrane with nanofluidic channels (5-6 Å). These channels can serve as facile and selective Li-ion diffusion pathways on the surfaces of lithium anodes, thereby ensuring stable lithium stripping/plating even at high areal current densities. CMP-modified lithium anodes (CMP-Li) exhibit cycle stability of 2550 h at an areal current density of 20 mA cm . Furthermore, CMP is readily amenable to solution-processing and spray coating, rendering it highly applicable to continuous roll-to-roll lithium metal treatment processes. Pouch cells with CMP-Li as the anode and LiNi Co Mn O (NCM811) as the cathode exhibits a stable energy density of 400 Wh kg .
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http://dx.doi.org/10.1002/adma.202006323DOI Listing
February 2021

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Brief Bioinform 2020 Dec 14. Epub 2020 Dec 14.

Peking University, China. He is currently a professor in the College of Pharmaceutical Sciences, Zhejiang University, China.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.
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http://dx.doi.org/10.1093/bib/bbaa321DOI Listing
December 2020

Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning.

Chemosphere 2021 Feb 26;265:129099. Epub 2020 Nov 26.

State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China. Electronic address:

The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.
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http://dx.doi.org/10.1016/j.chemosphere.2020.129099DOI Listing
February 2021

Remediation of contaminated soil and groundwater using chemical reduction and solidification/stabilization method: a case study.

Environ Sci Pollut Res Int 2021 Mar 22;28(10):12766-12779. Epub 2020 Oct 22.

Department of Civil Engineering, School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xianning West Road No. 28, Xi'an, 710049, Shaanxi, China.

This study presents a systematic on-site remediation case involving both heavy metal and organic contaminants in soil and groundwater in a historically industrial-used site in Shanghai, China. Lab-scale experiments and field tests were conducted to determine the optimum parameters for the removal of contaminants in soil and groundwater. It has been found that the remediation goal of hexavalent chromium in soil could be achieved with the mass content of added sodium hydrosulfite and ferrous sulfate reaching 3% + 6%. The total chromium in the groundwater was effectively removed, when the mass ratio of sodium metabisulfite was not less than 3 g/L, and the added quick lime made pH value not less than 9. The concentrations of arsenic and 1,2-dichloropropane in the groundwater decreased evidently after extraction and mixing of groundwater. The pH and calcium chloride dosage added should be larger than 9.5 and 5 g/L, respectively, to remove phosphate in groundwater. The removal efficiency of those contaminants was examined and evaluated after the on-site remediation. The results demonstrated that it was feasible to use the chemical reduction and solidification/stabilization methods for the on-site ex situ remediation of this site, which could be referenced for the realistic remediation of similar sites.
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http://dx.doi.org/10.1007/s11356-020-11337-3DOI Listing
March 2021

Knockdown of Long Noncoding RNA Represses Gallbladder Cancer Advancement by Regulating Expression Through Sponging .

Cancer Biother Radiopharm 2020 Oct 22. Epub 2020 Oct 22.

Department of Urology, Loudi Central Hospital of Hunan, Loudi, China.

Gallbladder cancer (GBC) is the most common biliary tract malignancy. Long noncoding RNA () and () have been reported to be involved in the progression of various cancers. However, the regulatory mechanism between and in GBC is unclear. The expression levels of , , and () mRNA were detected using quantitative real-time polymerase chain reaction. Cell proliferation, migration, invasion, and apoptosis were determined with MTT, transwell, or flow cytometry assays. The levels of protein, Bax, cleaved-casp-3, and Bcl-2 were determined by western blot analysis. The relationship between and or was verified through dual-luciferase reporter and/or RNA immunoprecipitation assays. Xenograft assay was performed to verify the role of . and were upregulated, whereas was downregulated in GBC tissues and cells. silencing decreased tumor growth and impeded proliferation, migration, invasion, and induced apoptosis of GBC cells . Notably, acted as a sponge for , which targeted in GBC cells. Moreover, enhancement reversed the repressive impact of mimic on the malignancy of GBC cells. regulated expression through adsorbing . Furthermore, elevation overturned silencing mediated the malignant behaviors of GBC cells. knockdown suppressed GBC progression through downregulating via sponging , providing an evidence for as a target for GBC treatment.
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http://dx.doi.org/10.1089/cbr.2020.4290DOI Listing
October 2020

Brachial-Ankle Pulse Wave Velocity is Related to the Total Cerebral Small-Vessel Disease Score in an Apparently Healthy Asymptomatic Population.

J Stroke Cerebrovasc Dis 2020 Nov 15;29(11):105221. Epub 2020 Aug 15.

Department of Senile Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Senile Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China. Electronic address:

Introduction: Cerebral small-vessel disease (CSVD) is an extensive cerebrovascular disease associated with many poor outcomes. Previous studies have shown that brachial-ankle pulse wave velocity (baPWV) is related to various neuroimaging signatures, but its association with the total CSVD burden remains unknown. We aimed to explore whether baPWV is related to the total CSVD score and to establish a cutoff for detecting the presence and severity of CSVD, which may guide clinical preventive measures.

Methods: We retrospectively selected 684 neurologically healthy participants to explore correlations between baPWV and the total CSVD score and each of its components (lacunes, white matter hyperintensity (WMH), perivascular space (PVS), and cerebral microbleeds (CMBs)). Subsequently, we established two receiver operating characteristic (ROC) curves to study the effectiveness of baPWV in predicting CSVD (scores 1-4) and severe CSVD (scores 3-4).

Results: The median baPWV was 13.16 m/s, which increased significantly with increasing scores (0-4). BaPWV was significantly higher among persons with each component of the total CSVD score than among those without any components. Multivariable ordinal logistic regression analyses showed that a one-unit (m/s) change in baPWV significantly increased the total CSVD score by 0.012. The optimal baPWV cutoffs for detecting CSVD and severe CSVD were 13.12 m/s and 15.63 m/s, respectively.

Conclusions: BaPWV was positively correlated with the total CSVD score, suggesting that baPWV measurement is a useful method for early diagnosis of CSVD, which may contribute to preventing and controlling CSVD progression in the general population of China.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2020.105221DOI Listing
November 2020

A Coplanar π-Extended Quinoxaline Based Hole-Transporting Material Enabling over 21 % Efficiency for Dopant-Free Perovskite Solar Cells.

Angew Chem Int Ed Engl 2021 Feb 30;60(5):2674-2679. Epub 2020 Nov 30.

Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Institute of Fine Chemicals, School of Chemistry and Molecular Engineering, East China University of Science & Technology, Shanghai, 200237, China.

Developing dopant-free hole transporting materials (HTMs) is of vital importance for addressing the notorious stability issue of perovskite solar cells (PSCs). However, efficient dopant-free HTMs are scarce. Herein, we improve the performance of dopant-free HTMs featuring with a quinoxaline core via rational π-extension. Upon incorporating rotatable or chemically fixed thienyl substitutes on the pyrazine ring, the resulting molecular HTMs TQ3 and TQ4 show completely different molecular arrangement as well as charge transporting capabilities. Comparing with TQ3, the coplanar π-extended quinoxaline based TQ4 endows enriched intermolecular interactions and stronger π-π stacking, thus achieving a higher hole mobility of 2.08×10  cm  V  s . It also shows matched energy levels and high thermal stability for application in PSCs. Planar n-i-p structured PSCs employing dopant-free TQ4 as HTM exhibits power conversion efficiency (PCE) over 21 % with excellent long-term stability.
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http://dx.doi.org/10.1002/anie.202013128DOI Listing
February 2021