Pub Date : 2023-10-27DOI: 10.1142/s2737416523500618
Mohamad Jemain Mohamad Ridhwan, Nurul Azmir Amir Hashim, Noraini Kasim, Nor Nadirah Abdullah, Nurul Alam Inayatsyah, Nor Hadiani Ismail, Syahrul Imran
{"title":"Development of a novel CYP3A4 classifier model via site of metabolism (SOM)-based molecular docking, multivariate analysis and molecular dynamics of known substrates and inhibitors","authors":"Mohamad Jemain Mohamad Ridhwan, Nurul Azmir Amir Hashim, Noraini Kasim, Nor Nadirah Abdullah, Nurul Alam Inayatsyah, Nor Hadiani Ismail, Syahrul Imran","doi":"10.1142/s2737416523500618","DOIUrl":"https://doi.org/10.1142/s2737416523500618","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1142/s273741652350062x
Jitender Singh, Pramod K Avti, Krishan L Khanduja, Radhika Dhawan, Namrata Sangwan, Arushi Chauhan
{"title":"Novel specific SARS-CoV-2 miRNAs Targeting Human Genes involved in COVID-19 Infection and their Regulation by Bemcentinib and Zavegepant: A Promising Evidence for RNA-Based Repurposing Therapeutic Strategy","authors":"Jitender Singh, Pramod K Avti, Krishan L Khanduja, Radhika Dhawan, Namrata Sangwan, Arushi Chauhan","doi":"10.1142/s273741652350062x","DOIUrl":"https://doi.org/10.1142/s273741652350062x","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"23 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.1142/s2737416523410053
Bo-Wei Zhao, Xiao-Rui Su, Dong-Xu Li, Guo-Dong Li, Peng-Wei Hu, Yong-Gang Zhao, Lun Hu
{"title":"A Graph Deep Learning-Based Framework for Drug-Disease Association Identification with Chemical Structure Similarities","authors":"Bo-Wei Zhao, Xiao-Rui Su, Dong-Xu Li, Guo-Dong Li, Peng-Wei Hu, Yong-Gang Zhao, Lun Hu","doi":"10.1142/s2737416523410053","DOIUrl":"https://doi.org/10.1142/s2737416523410053","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The biological properties of Physalis angulata L. include antibacterial, and antioxidant activity, anticancer and anti-inflammatory. The main goal of the research was to determine, using in silico methods, if some of the bioactive substances found in P. angulata L. extract were able to bind and inhibit the important protein/receptor. The Physalis angulata L. extract yielded significant in vitro-free radical scavenging activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) with IC[Formula: see text] value of 1.14 mg/ml, total phenolic content (TPC) value 133.96 ± 2.35 mg of gallic acid equivalent (GAE/g) and TFC value 47.6 ± 5.08 mg of quercetin equivalent (QE/g), respectively. The antibacterial activity was modest when compared with antibiotics controls. The extract was more effective on gram-positive Staphylococcus aureus than gram Escherichia coli yielding 11.367 ± 0.9 (mm) and 7.102 ± 0.5 (mm), respectively, at a 1 mg/mL concentration. The LC-HRMS analysis of the plant extract showed the most responsive compounds (30) that were present were selected to get the hit compound(s) on all target proteins viz., lipoxygenase-3, cytochrome P450, DNA gyrase topoisomerase II and histone acetyltransferase. Computational approaches revealed the low binding affinity of (+)-gallocatechin among 30 identified compounds on all target proteins. All identified compounds have good pharmacokinetic characteristics on ADMET parameters. Based on this study, P. angulata L. extract is a promising source of biological activity with great potential therapeutic use as an antibacterial and antioxidant.
{"title":"<i>In silico</i> molecular docking approach and in vitro antioxidant and antimicrobial activity of <i>Physalis angulata</i> L. extract","authors":"Riuh Wardhani, Cici Darsih, Ade Chandra Iwansyah, Ashri Indriati, Hazrulrizawati Abdul Hamid, Dirayah Rauf Husain","doi":"10.1142/s2737416523500564","DOIUrl":"https://doi.org/10.1142/s2737416523500564","url":null,"abstract":"The biological properties of Physalis angulata L. include antibacterial, and antioxidant activity, anticancer and anti-inflammatory. The main goal of the research was to determine, using in silico methods, if some of the bioactive substances found in P. angulata L. extract were able to bind and inhibit the important protein/receptor. The Physalis angulata L. extract yielded significant in vitro-free radical scavenging activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH) with IC[Formula: see text] value of 1.14 mg/ml, total phenolic content (TPC) value 133.96 ± 2.35 mg of gallic acid equivalent (GAE/g) and TFC value 47.6 ± 5.08 mg of quercetin equivalent (QE/g), respectively. The antibacterial activity was modest when compared with antibiotics controls. The extract was more effective on gram-positive Staphylococcus aureus than gram Escherichia coli yielding 11.367 ± 0.9 (mm) and 7.102 ± 0.5 (mm), respectively, at a 1 mg/mL concentration. The LC-HRMS analysis of the plant extract showed the most responsive compounds (30) that were present were selected to get the hit compound(s) on all target proteins viz., lipoxygenase-3, cytochrome P450, DNA gyrase topoisomerase II and histone acetyltransferase. Computational approaches revealed the low binding affinity of (+)-gallocatechin among 30 identified compounds on all target proteins. All identified compounds have good pharmacokinetic characteristics on ADMET parameters. Based on this study, P. angulata L. extract is a promising source of biological activity with great potential therapeutic use as an antibacterial and antioxidant.","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"90 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135514288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-20DOI: 10.1142/s273741652350059x
Ghazi Elamin, Opeyemi S. Soremekun, Shaban R. M. Sayed, Peter A. Sidhom, Mahmoud A. A. Ibrahim, Muhammad Naeem Ahmed, Mahmoud E.S. Soliman
{"title":"Sequence-based Mechanistic Resolution of Amino Acid Replacement and Impact on the Activities of Peptide-Based Derivatives Targeting CXCR4 for the Treatment of Waldenstrom's Macroglobulinemia","authors":"Ghazi Elamin, Opeyemi S. Soremekun, Shaban R. M. Sayed, Peter A. Sidhom, Mahmoud A. A. Ibrahim, Muhammad Naeem Ahmed, Mahmoud E.S. Soliman","doi":"10.1142/s273741652350059x","DOIUrl":"https://doi.org/10.1142/s273741652350059x","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1142/s2737416523500576
Dongyun Gao, Jun Yao, Xuefeng Zhou, Xia Zhang, Linlin Zhou, Qiangrong Wang, Shan Li, Xi Ding
Human epidermal growth factor receptor mutation variant III (EGFR[Formula: see text] is a cancer-specific cell surface oncogenic marker and has been observed to be widely involved in the formation, progression and metastasis of lung cancer and some other tumors. Previously, a massive quantity of EGFR[Formula: see text]-binding peptides were enriched via random phage display (RPD) targeted against the protein. In this study, we reported rational discovery of 12-mer peptides with high affinity to EGFR[Formula: see text] and strong selectivity for EGFR[Formula: see text] over wild-type EGFR (EGFR[Formula: see text]. A combinatorial peptide library was designed to target EGFR[Formula: see text] based on over ten thousands of known EGFR[Formula: see text] binders enriched from RPD analysis, and a virtual high-throughput screening protocol was then systematically performed against the library to derive those potential candidates, which were further examined rigorously at structural and energetic levels to identify few promising hits. Anisotropy binding assays were carried out to substantiate the computational findings. Consequently, eight 12-mer peptides were designed as effective binders that can target the EGFR[Formula: see text] extracellular subdomain 3 (SD3). In particular, two potent peptides (T1: FLHRYEIVTSYF and T3: FLQKYEWNTSYW) were found to have a high affinity to EGFR[Formula: see text] and a good selectivity for EGFR[Formula: see text] over EGFR WT . Structural analysis revealed that the peptide-binding site can be divided into hydrophobic, polar and mixed regions, which correspond to the nonpolar [Formula: see text]-terminal section, polar/charged middle section and hybrid C-terminal section of the peptide. The peptide selectivity originated from the middle section, which can form a different hydrogen bond network between the two proteins upon the mutating perturbation, whereas the N- and C-terminal sections are primarily responsible for the peptide stability but not specificity.
{"title":"Computational Design, Combinatorial Screening and Experimental Analysis of Lung Cancer EGFR<sup>VIII</sup>-binding Peptides","authors":"Dongyun Gao, Jun Yao, Xuefeng Zhou, Xia Zhang, Linlin Zhou, Qiangrong Wang, Shan Li, Xi Ding","doi":"10.1142/s2737416523500576","DOIUrl":"https://doi.org/10.1142/s2737416523500576","url":null,"abstract":"Human epidermal growth factor receptor mutation variant III (EGFR[Formula: see text] is a cancer-specific cell surface oncogenic marker and has been observed to be widely involved in the formation, progression and metastasis of lung cancer and some other tumors. Previously, a massive quantity of EGFR[Formula: see text]-binding peptides were enriched via random phage display (RPD) targeted against the protein. In this study, we reported rational discovery of 12-mer peptides with high affinity to EGFR[Formula: see text] and strong selectivity for EGFR[Formula: see text] over wild-type EGFR (EGFR[Formula: see text]. A combinatorial peptide library was designed to target EGFR[Formula: see text] based on over ten thousands of known EGFR[Formula: see text] binders enriched from RPD analysis, and a virtual high-throughput screening protocol was then systematically performed against the library to derive those potential candidates, which were further examined rigorously at structural and energetic levels to identify few promising hits. Anisotropy binding assays were carried out to substantiate the computational findings. Consequently, eight 12-mer peptides were designed as effective binders that can target the EGFR[Formula: see text] extracellular subdomain 3 (SD3). In particular, two potent peptides (T1: FLHRYEIVTSYF and T3: FLQKYEWNTSYW) were found to have a high affinity to EGFR[Formula: see text] and a good selectivity for EGFR[Formula: see text] over EGFR WT . Structural analysis revealed that the peptide-binding site can be divided into hydrophobic, polar and mixed regions, which correspond to the nonpolar [Formula: see text]-terminal section, polar/charged middle section and hybrid C-terminal section of the peptide. The peptide selectivity originated from the middle section, which can form a different hydrogen bond network between the two proteins upon the mutating perturbation, whereas the N- and C-terminal sections are primarily responsible for the peptide stability but not specificity.","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135666823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1142/s2737416523420139
A Arunkumar
A new generation of metal-free organic dyes with a range of donor (D1) and acceptors (A1-A3) were designed and examined for dye-sensitized solar cells (DSSCs) based on (3a) dye as a literature. Triphenylamine (TPA), thiophene ([Formula: see text] and 2-cyanoacrylic acid groups each perform the roles of an acceptor (A), donor (D) and spacer in order to produce a D-[Formula: see text]-A system. To investigate the intramolecular charge transfer (ICT), electronic distribution, ultra-violet visible (UV–Vis) absorption wavelengths, molecular electrostatic potential (MEP) and photovoltaic (PV) parameters of the D1 and A1–A3 molecules, density functional theory (DFT) and time-dependent DFT (TD-DFT) were used. The classification of the tunable donor D1 and A1–A3 determines the PV performance of the dye molecules. Results show that the A2 dye replacement group increases the performance of PV cells via red-shifting absorption spectra. Also, when compared to 3a, A2 dye have lower energy gap ([Formula: see text] and superior UV–Vis spectra that cover the full visible range. These results demonstrate the viability of molecular tailoring as an approach to improve D-[Formula: see text]-A sensitizer proposal for efficient DSSCs fabrication.
{"title":"Computational Study on D-π-A-based Electron Donating and Withdrawing Effect of Metal-Free Organic Dye Sensitizers for Efficient Dye-Sensitized Solar Cells","authors":"A Arunkumar","doi":"10.1142/s2737416523420139","DOIUrl":"https://doi.org/10.1142/s2737416523420139","url":null,"abstract":"A new generation of metal-free organic dyes with a range of donor (D1) and acceptors (A1-A3) were designed and examined for dye-sensitized solar cells (DSSCs) based on (3a) dye as a literature. Triphenylamine (TPA), thiophene ([Formula: see text] and 2-cyanoacrylic acid groups each perform the roles of an acceptor (A), donor (D) and spacer in order to produce a D-[Formula: see text]-A system. To investigate the intramolecular charge transfer (ICT), electronic distribution, ultra-violet visible (UV–Vis) absorption wavelengths, molecular electrostatic potential (MEP) and photovoltaic (PV) parameters of the D1 and A1–A3 molecules, density functional theory (DFT) and time-dependent DFT (TD-DFT) were used. The classification of the tunable donor D1 and A1–A3 determines the PV performance of the dye molecules. Results show that the A2 dye replacement group increases the performance of PV cells via red-shifting absorption spectra. Also, when compared to 3a, A2 dye have lower energy gap ([Formula: see text] and superior UV–Vis spectra that cover the full visible range. These results demonstrate the viability of molecular tailoring as an approach to improve D-[Formula: see text]-A sensitizer proposal for efficient DSSCs fabrication.","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1142/s2737416523410041
A S Aruna, K R Remesh Babu, K Deepthi
The global spread of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan in December 2019, created a massive health crisis, and disrupted the world economy. Much research has been conducted to discover drugs, develop vaccines, and find repurposable drugs against the disease. Computational drug repurposing, the process of determining new uses for approved drugs through computational techniques, becomes an effective solution to fight the COVID-19 pandemic. This study aims to investigate and prioritize potential drugs against SARS-CoV-2 through an integrated network-based approach. We propose an ensemble approach based on network inference and inductive matrix completion (NIMCVDA) for virus–drug association prediction to identify antivirals against COVID-19. We constructed a heterogeneous drug–virus network using intra-similarities of virus genomic sequences and drug chemical structures and existing associations between viruses and drugs. A network inference method is used to infer missing drug–virus edges. Based on this, existing drug–virus association matrix is reconstructed. Finally, more accurate association scores between drugs and viruses are computed using the inductive matrix completion algorithm. The proposed method achieved an AUC of 0.9020 on five-fold cross-validation and 0.8786 on leave-one-out cross-validation. We compared the performance of the model with related approaches. In addition, we carried out case studies on the top-predicted drugs and implemented our model with other datasets to verify prediction performance. Our work prioritized repurposable drugs to battle with COVID-19 epidemic. The cross-validation results and case studies illustrate that the top-predicted drugs are strong candidates for further biological tests.
{"title":"An ensemble approach for prioritizing antivirals against COVID-19 via heterogeneous network inference-based inductive matrix completion","authors":"A S Aruna, K R Remesh Babu, K Deepthi","doi":"10.1142/s2737416523410041","DOIUrl":"https://doi.org/10.1142/s2737416523410041","url":null,"abstract":"The global spread of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan in December 2019, created a massive health crisis, and disrupted the world economy. Much research has been conducted to discover drugs, develop vaccines, and find repurposable drugs against the disease. Computational drug repurposing, the process of determining new uses for approved drugs through computational techniques, becomes an effective solution to fight the COVID-19 pandemic. This study aims to investigate and prioritize potential drugs against SARS-CoV-2 through an integrated network-based approach. We propose an ensemble approach based on network inference and inductive matrix completion (NIMCVDA) for virus–drug association prediction to identify antivirals against COVID-19. We constructed a heterogeneous drug–virus network using intra-similarities of virus genomic sequences and drug chemical structures and existing associations between viruses and drugs. A network inference method is used to infer missing drug–virus edges. Based on this, existing drug–virus association matrix is reconstructed. Finally, more accurate association scores between drugs and viruses are computed using the inductive matrix completion algorithm. The proposed method achieved an AUC of 0.9020 on five-fold cross-validation and 0.8786 on leave-one-out cross-validation. We compared the performance of the model with related approaches. In addition, we carried out case studies on the top-predicted drugs and implemented our model with other datasets to verify prediction performance. Our work prioritized repurposable drugs to battle with COVID-19 epidemic. The cross-validation results and case studies illustrate that the top-predicted drugs are strong candidates for further biological tests.","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1142/s2737416523500588
Sameena Gul, Shabbir Muhammad, Muhammad Irfan, Tareg M Belali, A. R. Chaudhry, Muhammad Khan
{"title":"Discovery of Potential Natural STAT3 Inhibitors: An in silico Molecular Docking and Molecular Dynamics Study","authors":"Sameena Gul, Shabbir Muhammad, Muhammad Irfan, Tareg M Belali, A. R. Chaudhry, Muhammad Khan","doi":"10.1142/s2737416523500588","DOIUrl":"https://doi.org/10.1142/s2737416523500588","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":"4 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139322164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1142/s2737416523500540
Mahendra Gowdru Srinivasa, Shivakumar, Udaya Kumar, C. Mehta, U. Nayak, B. C. Revanasiddappa
{"title":"In silico studies of (Z)-3-(2-chloro-4-nitrophenyl)-5-(4-nitrobenzylidene)-2-thioxothiazolidin-4-one derivatives as PPAR-α agonist: Design, Molecular Docking, MM-GBSA Assay, Toxicity Predictions, DFT Calculations and MD Simulation Studies","authors":"Mahendra Gowdru Srinivasa, Shivakumar, Udaya Kumar, C. Mehta, U. Nayak, B. C. Revanasiddappa","doi":"10.1142/s2737416523500540","DOIUrl":"https://doi.org/10.1142/s2737416523500540","url":null,"abstract":"","PeriodicalId":15603,"journal":{"name":"Journal of Computational Biophysics and Chemistry","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49348636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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