Pub Date : 2024-08-06DOI: 10.2174/1573409920666230816090626
Saransh Rohilla, Shruti Jain
Background: Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for Brain tumors are proposed in this paper.
Methods: A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
Results: The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.
Conclusion: The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
{"title":"Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning","authors":"Saransh Rohilla, Shruti Jain","doi":"10.2174/1573409920666230816090626","DOIUrl":"10.2174/1573409920666230816090626","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The automatic and accurate detection and segmentation methods for Brain tumors are proposed in this paper.</p><p><strong>Methods: </strong>A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.</p><p><strong>Results: </strong>The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were compared based on manual segmentation and existing segmentation techniques. The tumor mask was also individually compared to the ground truth using the SSIM value. The proposed detection and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results were consensus.</p><p><strong>Conclusion: </strong>The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.</p>","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10367852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-03DOI: 10.2174/0115734099283410240406064042
M. Gnana Ruba Priya, Jessica Manisha, Lal Prasanth M.L, Seema S. Rathore, Raja Solomon Viswas
: Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.
:天然植物资源对开发多种抗癌药物至关重要，如长春新碱、长春碱、长春瑞滨、多西他赛、紫杉醇、喜树碱、依托泊苷和替尼泊苷。然而，由于不良反应、耐药性和靶向特异性等原因，各种化疗均告失败。目前，研究人员正致力于开发利用天然化合物来克服这些问题的药物。这些药物可以影响多个靶点，减少不良反应，并对多种癌症类型有效。研发新药是一个非常复杂、昂贵和耗时的过程。传统的药物发现方法需要长达 15 年的时间才能让一种新药进入市场，成本超过 10 亿美元。然而，近年来计算机辅助药物发现（CADD）的发展改变了这一局面。本文旨在全面介绍从天然产物中发现抗癌药物的不同 CADD 方法。本综述使用了来自不同来源的数据，包括 Science Direct、Elsevier、NCBI 和 Web of Science。硅学技术和优化算法可以为药物研发提供多种解决方案。基于结构的药物设计技术被广泛应用于了解化学成分的分子级相互作用和确定新药线索。这篇综述将讨论 CADD 的概念、水下工具、药物发现中的虚拟筛选以及天然产品作为抗癌疗法的概念。此外，还将提供已确定分子的代表性实例。
{"title":"Computer-Aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review","authors":"M. Gnana Ruba Priya, Jessica Manisha, Lal Prasanth M.L, Seema S. Rathore, Raja Solomon Viswas","doi":"10.2174/0115734099283410240406064042","DOIUrl":"https://doi.org/10.2174/0115734099283410240406064042","url":null,"abstract":": Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"13 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140842483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.2174/0115734099288771240419110716
Lulu Wu, Bo Xu, Yu Qi, Changjin Yuan
Introduction: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Traditional Chinese medicine, known for its multi-target and multi-pathway characteristics, offers a potential treatment approach for NSCLC. Objective: This study aimed to explore the mechanism of the competitive endogenous network of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' in treating NSCLC through bioinformatics analysis and in vitro experiments. objective: This study aimed to explore the mechanism of the competitive endogenous network of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' in treating NSCLC through bioinformatics analysis and in vitro experiments. Materials and Methods: Various databases and ceRNA networks were utilized to collect and screen components and target genes, molecular docking and molecular dynamics simulations to determine the binding ability of ligand-receptor complexes. In vitro experiments were conducted to validate the effects of active ingredients of 'Scutellaria barbata D.Don-Houttuynia cordata- Radix Scutellariae' on non-small cell lung cancer cell line A549. method: Various databases and ceRNA networks were utilized to collect and screen components and target genes, molecular docking and molecular dynamics simulations to determine the binding ability of ligand-receptor complexes. In vitro experiments were conducted to validate the effects of active ingredients of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' on non-small cell lung cancer cell line A549. Results: The key target proteins CCL2, EDN1, MMP9, PPARG, and SPP1 were docked well with their corresponding TCM ligands. Among the ligand-receptor complexes, MMP9-Luteolin and MMP9-Quercetin demonstrated the weaking binding force, while the SPP1-Quercetin complex, associated with NSCLC prognosis, exhibited stable structure formation through hydrogen bond interaction during MD simulation. In vitro experiments confirmed the inhibitory effect of Quercetin on SPP1 expression, as well as the proliferation and migration of A549 cells. Conclusion: The findings suggest that 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' may potentially treat lung cancer by suppressing the expression of SPP1. This study provides valuable insights and novel research directions for understanding the mechanism of traditional Chinese medicine in combating lung cancer.
{"title":"Study on the Mechanism of Competing Endogenous Network of 'Scutellaria barbata D.Don-Houttuynia cordata- Radix Scutellariae' in the Treatment of NSCLC based on Bioinformatics, Molecular Dynamics and Experimental Verification","authors":"Lulu Wu, Bo Xu, Yu Qi, Changjin Yuan","doi":"10.2174/0115734099288771240419110716","DOIUrl":"https://doi.org/10.2174/0115734099288771240419110716","url":null,"abstract":"Introduction: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. Traditional Chinese medicine, known for its multi-target and multi-pathway characteristics, offers a potential treatment approach for NSCLC. Objective: This study aimed to explore the mechanism of the competitive endogenous network of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' in treating NSCLC through bioinformatics analysis and in vitro experiments. objective: This study aimed to explore the mechanism of the competitive endogenous network of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' in treating NSCLC through bioinformatics analysis and in vitro experiments. Materials and Methods: Various databases and ceRNA networks were utilized to collect and screen components and target genes, molecular docking and molecular dynamics simulations to determine the binding ability of ligand-receptor complexes. In vitro experiments were conducted to validate the effects of active ingredients of 'Scutellaria barbata D.Don-Houttuynia cordata- Radix Scutellariae' on non-small cell lung cancer cell line A549. method: Various databases and ceRNA networks were utilized to collect and screen components and target genes, molecular docking and molecular dynamics simulations to determine the binding ability of ligand-receptor complexes. In vitro experiments were conducted to validate the effects of active ingredients of 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' on non-small cell lung cancer cell line A549. Results: The key target proteins CCL2, EDN1, MMP9, PPARG, and SPP1 were docked well with their corresponding TCM ligands. Among the ligand-receptor complexes, MMP9-Luteolin and MMP9-Quercetin demonstrated the weaking binding force, while the SPP1-Quercetin complex, associated with NSCLC prognosis, exhibited stable structure formation through hydrogen bond interaction during MD simulation. In vitro experiments confirmed the inhibitory effect of Quercetin on SPP1 expression, as well as the proliferation and migration of A549 cells. Conclusion: The findings suggest that 'Scutellaria barbata D.Don-Houttuynia cordata-Radix Scutellariae' may potentially treat lung cancer by suppressing the expression of SPP1. This study provides valuable insights and novel research directions for understanding the mechanism of traditional Chinese medicine in combating lung cancer.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"21 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.2174/0115734099288803240416103536
Mohammad Reza Keyvanpour, Soheila Mehrmolaei, Faraneh Haddadi
Background:: In recent years, analyzing complex biological networks to predict future links in such networks has attracted the attention of many medical and computer science researchers. The discovery of new drugs is one of the application cases for predicting future connections in biological networks. The operation of drug-target interactions prediction (DTIP) can be considered a fundamental step in identifying potential interactions between drug and target to identify new drugs. Objective:: The previous studies reveal that predictions are made based on known interactions using computational methods to solve the cost problem and avoid blind study of all interactions. But, there seem to be challenges such as the lack of confirmed negative samples and the low accuracy in some computational methods. Thus, we have proposed an efficient and hybrid approach called MKPUL-BLM to manage some of the aforementioned challenges for predicting drug-target interactions. Methods:: The MKPUL-BLM combins multi-kernel and positive unlabeled learning (PUL) approaches. Our method uses more information to increase accuracy, in addition to minimizing small similarities using network information. Also, potential negative samples are produced using a PUL approach because of lacking negative laboratory samples. Finally, labels are expanded via a semi-supervised. Results:: Our method improved to 0.98 and 0.94 in the old interactions set for the ROCAUC and AUPR criteria, respectively. Also, this method enhanced ROCAUC and AUPR criteria by 0.89 and 0.77 for the new interactions set. Conclusion:: The MKPUL-BLM can be considered an efficient alternative to achieve more reliable predictions in the field of DTIP.
{"title":"An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions","authors":"Mohammad Reza Keyvanpour, Soheila Mehrmolaei, Faraneh Haddadi","doi":"10.2174/0115734099288803240416103536","DOIUrl":"https://doi.org/10.2174/0115734099288803240416103536","url":null,"abstract":"Background:: In recent years, analyzing complex biological networks to predict future links in such networks has attracted the attention of many medical and computer science researchers. The discovery of new drugs is one of the application cases for predicting future connections in biological networks. The operation of drug-target interactions prediction (DTIP) can be considered a fundamental step in identifying potential interactions between drug and target to identify new drugs. Objective:: The previous studies reveal that predictions are made based on known interactions using computational methods to solve the cost problem and avoid blind study of all interactions. But, there seem to be challenges such as the lack of confirmed negative samples and the low accuracy in some computational methods. Thus, we have proposed an efficient and hybrid approach called MKPUL-BLM to manage some of the aforementioned challenges for predicting drug-target interactions. Methods:: The MKPUL-BLM combins multi-kernel and positive unlabeled learning (PUL) approaches. Our method uses more information to increase accuracy, in addition to minimizing small similarities using network information. Also, potential negative samples are produced using a PUL approach because of lacking negative laboratory samples. Finally, labels are expanded via a semi-supervised. Results:: Our method improved to 0.98 and 0.94 in the old interactions set for the ROCAUC and AUPR criteria, respectively. Also, this method enhanced ROCAUC and AUPR criteria by 0.89 and 0.77 for the new interactions set. Conclusion:: The MKPUL-BLM can be considered an efficient alternative to achieve more reliable predictions in the field of DTIP.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"21 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The design of an epitope-based vaccine against diphtheria toxin (DT) originated from the idea that many strong binder epitopes may be structurally located in the depth of DT. Subsequently, many ineffective antibodies may be produced by the presentation of those epitopes to T and B lymphocytes. The other critical issue is the population coverage of a vaccine that has been neglected in traditional vaccines. Objective: Given the issues above, our study aimed to design a peptide-based diphtheria vaccine, considering the issues of unwanted epitopes and population coverage. Methods: The frequencies of pre-determined HLA alleles were listed. A country in which almost all HLA alleles had been determined in almost all geographical distribution was selected. The epitopes within the sequence of diphtheria toxin were predicted by the NetMHCIIPan server based on the selected HLA alleles. Strong binder epitopes on the surface of diphtheria toxin were selected by structural epitope mapping. The epitopes, which cover almost all the human population for each of the HLA alleles in the candidate country, were then selected as epitopebased vaccines. Results: At first, 793 strong binder epitopes were predicted, of which 82 were surface epitopes. Nine surface epitopes whose amino acids had extruding side chains were selected. Finally, 2 epitopes had the most population coverage and were suggested as a di-epitope diphtheria vaccine. The population coverage of the di-epitope vaccine in France and the world was 100 and 99.24 %, respectively. HLA-DP had the most roles in epitope presentation.
{"title":"Designing a Novel di-epitope Diphtheria Vaccine: A Rational Structural Immunoinformatics Approach","authors":"Mahsa Shadmani, Atefeh Ghasemnejad, Samira Bazmara, Kamran Pooshang Bagheri","doi":"10.2174/0115734099294259240411073449","DOIUrl":"https://doi.org/10.2174/0115734099294259240411073449","url":null,"abstract":"Background: The design of an epitope-based vaccine against diphtheria toxin (DT) originated from the idea that many strong binder epitopes may be structurally located in the depth of DT. Subsequently, many ineffective antibodies may be produced by the presentation of those epitopes to T and B lymphocytes. The other critical issue is the population coverage of a vaccine that has been neglected in traditional vaccines. Objective: Given the issues above, our study aimed to design a peptide-based diphtheria vaccine, considering the issues of unwanted epitopes and population coverage. Methods: The frequencies of pre-determined HLA alleles were listed. A country in which almost all HLA alleles had been determined in almost all geographical distribution was selected. The epitopes within the sequence of diphtheria toxin were predicted by the NetMHCIIPan server based on the selected HLA alleles. Strong binder epitopes on the surface of diphtheria toxin were selected by structural epitope mapping. The epitopes, which cover almost all the human population for each of the HLA alleles in the candidate country, were then selected as epitopebased vaccines. Results: At first, 793 strong binder epitopes were predicted, of which 82 were surface epitopes. Nine surface epitopes whose amino acids had extruding side chains were selected. Finally, 2 epitopes had the most population coverage and were suggested as a di-epitope diphtheria vaccine. The population coverage of the di-epitope vaccine in France and the world was 100 and 99.24 %, respectively. HLA-DP had the most roles in epitope presentation.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"6 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-19DOI: 10.2174/0115734099282388240405055003
Jianwei Ren, Zhiting Mo, Zhengsha Huang, Shangze Li
Background: Network pharmacology is a novel approach that uses bioinformatics to predict multitarget drugs and ingredient-target interactions in various diseases. A thorough search of previously published studies revealed that Hedyotis diffusa Willd (HDW) and Astragalus membranaceus (AM) possess anticancer activity. Colon cancer (CC) is one of the most common malignant tumors of the digestive tract and occurs in the colon. Herein, we explored the effect of two drugs in the treatment of CC. Objective: The present study aimed to predict and verify the effect of these two drugs in the treatment of CC. Methods: To explore the molecular mechanisms of the “HDW-AM” drug in the treatment of CC, we analyzed its principal efficiency in terms of ingredients, target spots, and pathways via network pharmacology, molecular docking, and experimental verification. The ingredients and their gene target sites were searched and screened through the TCMSP platform according to specific filtering conditions. Subsequently, components corresponding to the gene targets were chosen to construct the drug component-target network. The GEO (Gene Expression Omnibus) dataset was used to collect and screen for gene chips under CC and normal conditions, obtain differential genes, and construct a volcano map. The intersection genes between drug and disease targets were screened, the “.tsv” file was downloaded from the STRING platform and imported into Cytoscape 3.8.0 for visualization, a protein-protein interaction (PPI) network was constructed, the core targets were identified, and the common components with core targets were docked through Autodock Tools-1.5.6. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out through the Metascape platform to determine the major pathways. The CCK-8 (Cell Counting Kit-8) assay verified the effect of AKT1 on cell proliferation after treatment with quercetin. Results: After the screening, 3658 DEGs (1841 downregulated and 1817 upregulated) were obtained from the GSE75970 gene chip; 21 active components and 220 targets were identified from the drugs. Subsequently, ten core genes (including AKT1, P53, and CASP3) and six major components were screened. GO functional analysis and KEGG analysis revealed that “HDWAM” regulates cell migration and motility through the combination of a transcription regulator complex, membrane rafts, vesicle lumen, and protein kinases via the MAPK, PI3K-Akt, and IL17 signaling pathways. The molecular docking results suggested that quercetin binds to AKT1, TP53, TNF, and CASP3. HDW-AM may exert a therapeutic effect on CC by modulating AKT1, TP53, TNF, and CASP3 and through signaling pathways. A CCK-8 cytotoxicity assay verified that quercetin affects cell viability through AKT1. Conclusions: The current study provides a theoretical basis for an in-depth investigation into the molecular mechanism of the “HDW-AM” drug in CC treatment via network pharmacology, molecular
{"title":"Hedyotis diffusa Willd and Astragalus membranaceus May Exert Anti-colon Cancer Effects by Affecting AKTI Expression, as Determined by Network Pharmacology and Molecular Docking","authors":"Jianwei Ren, Zhiting Mo, Zhengsha Huang, Shangze Li","doi":"10.2174/0115734099282388240405055003","DOIUrl":"https://doi.org/10.2174/0115734099282388240405055003","url":null,"abstract":"Background: Network pharmacology is a novel approach that uses bioinformatics to predict multitarget drugs and ingredient-target interactions in various diseases. A thorough search of previously published studies revealed that Hedyotis diffusa Willd (HDW) and Astragalus membranaceus (AM) possess anticancer activity. Colon cancer (CC) is one of the most common malignant tumors of the digestive tract and occurs in the colon. Herein, we explored the effect of two drugs in the treatment of CC. Objective: The present study aimed to predict and verify the effect of these two drugs in the treatment of CC. Methods: To explore the molecular mechanisms of the “HDW-AM” drug in the treatment of CC, we analyzed its principal efficiency in terms of ingredients, target spots, and pathways via network pharmacology, molecular docking, and experimental verification. The ingredients and their gene target sites were searched and screened through the TCMSP platform according to specific filtering conditions. Subsequently, components corresponding to the gene targets were chosen to construct the drug component-target network. The GEO (Gene Expression Omnibus) dataset was used to collect and screen for gene chips under CC and normal conditions, obtain differential genes, and construct a volcano map. The intersection genes between drug and disease targets were screened, the “.tsv” file was downloaded from the STRING platform and imported into Cytoscape 3.8.0 for visualization, a protein-protein interaction (PPI) network was constructed, the core targets were identified, and the common components with core targets were docked through Autodock Tools-1.5.6. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out through the Metascape platform to determine the major pathways. The CCK-8 (Cell Counting Kit-8) assay verified the effect of AKT1 on cell proliferation after treatment with quercetin. Results: After the screening, 3658 DEGs (1841 downregulated and 1817 upregulated) were obtained from the GSE75970 gene chip; 21 active components and 220 targets were identified from the drugs. Subsequently, ten core genes (including AKT1, P53, and CASP3) and six major components were screened. GO functional analysis and KEGG analysis revealed that “HDWAM” regulates cell migration and motility through the combination of a transcription regulator complex, membrane rafts, vesicle lumen, and protein kinases via the MAPK, PI3K-Akt, and IL17 signaling pathways. The molecular docking results suggested that quercetin binds to AKT1, TP53, TNF, and CASP3. HDW-AM may exert a therapeutic effect on CC by modulating AKT1, TP53, TNF, and CASP3 and through signaling pathways. A CCK-8 cytotoxicity assay verified that quercetin affects cell viability through AKT1. Conclusions: The current study provides a theoretical basis for an in-depth investigation into the molecular mechanism of the “HDW-AM” drug in CC treatment via network pharmacology, molecular ","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"51 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The role of Forkhead Box D2 (FOXD2) in head and neck squamous cell carcinoma (HNSC) has never been studied. Object: Our object was to explore the role of FOXD2 in HNSC. Methods: Clinical data for patients with HNSC was obtained from TCGA. Our study examined the atypical expression of FOXD2 in both HNSC and pan-cancer, along with its diagnostic and prognostic implications, as well as the association between FOXD2 expression and clinical characteristics, immune infiltration, immune checkpoint genes, and MSI. Gene set enrichment analysis (GESA) was used to investigate the potential regulation network of FOXD2 in HNSC. We analyze the genomic alterations of FOXD2 in HNSC. GSE13397 and qRT-PCR were used for the validation of FOXD2 expression. Results: FOXD2 was aberrantly expressed in 24 tumors. FOXD2 was significantly up-regulated in HNSC compared to normal head and neck tissue (p < 0.001). High FOXD2 expression was associated with the histologic grade of the patient with HNSC (p < 0.001), lymphovascular infiltration (p = 0.002) and lymph node neck dissection (p = 0.002). In HNSC, an autonomous correlation between FOXD2 expression and OS was observed (HR: 1.36; 95% CI: 1.04-1.78; p = 0.026). FOXD2 was associated with the neuronal system, neuroactive ligand-receptor interaction, and retinoblastoma gene in cancer. FOXD2 was associated with immune infiltration, immune checkpoints, and MSI. The somatic mutation rate of FOXD2 in HNSC was 0.2%. FOXD2 was significantly up-regulated in HNSC cell lines. Conclusion: Our findings suggest that FOXD2 has the potential to serve as a prognostic biomarker and immunotherapeutic target for individuals with HNSC.
{"title":"Comprehensive Analysis and Experimental Validation of FOXD2 as a Novel Potential Prognostic Biomarker Associated with Immune Infiltration in Head and Neck Squamous Cell Carcinoma","authors":"Hanping He, Feng Yuan, Ying Li, Guoliang Pi, Hongwei Shi, Yanping Li, Guang Han","doi":"10.2174/0115734099302492240405065505","DOIUrl":"https://doi.org/10.2174/0115734099302492240405065505","url":null,"abstract":"Background: The role of Forkhead Box D2 (FOXD2) in head and neck squamous cell carcinoma (HNSC) has never been studied. Object: Our object was to explore the role of FOXD2 in HNSC. Methods: Clinical data for patients with HNSC was obtained from TCGA. Our study examined the atypical expression of FOXD2 in both HNSC and pan-cancer, along with its diagnostic and prognostic implications, as well as the association between FOXD2 expression and clinical characteristics, immune infiltration, immune checkpoint genes, and MSI. Gene set enrichment analysis (GESA) was used to investigate the potential regulation network of FOXD2 in HNSC. We analyze the genomic alterations of FOXD2 in HNSC. GSE13397 and qRT-PCR were used for the validation of FOXD2 expression. Results: FOXD2 was aberrantly expressed in 24 tumors. FOXD2 was significantly up-regulated in HNSC compared to normal head and neck tissue (p < 0.001). High FOXD2 expression was associated with the histologic grade of the patient with HNSC (p < 0.001), lymphovascular infiltration (p = 0.002) and lymph node neck dissection (p = 0.002). In HNSC, an autonomous correlation between FOXD2 expression and OS was observed (HR: 1.36; 95% CI: 1.04-1.78; p = 0.026). FOXD2 was associated with the neuronal system, neuroactive ligand-receptor interaction, and retinoblastoma gene in cancer. FOXD2 was associated with immune infiltration, immune checkpoints, and MSI. The somatic mutation rate of FOXD2 in HNSC was 0.2%. FOXD2 was significantly up-regulated in HNSC cell lines. Conclusion: Our findings suggest that FOXD2 has the potential to serve as a prognostic biomarker and immunotherapeutic target for individuals with HNSC.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"49 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.2174/0115734099292513240404091734
Xuecheng Yu, Kun Shi, Bin Wu, Zengxiang Gao, Jiyuan Tu, Yan Cao, Linlin Chen, Guosheng Cao
Background: Shenfu injection was derived from the classical Chinese medicine formula ‘Shenfu decoction’, which was widely used in the treatment of cardiovascular and cerebrovascular diseases in clinical practice. background: Shenfu injection is derived from the classical Chinese medicine formula ‘shenfu decoction’, which is widely used in the treatment of cardiovascular and cerebrovascular diseases in clinical practice. Objectives: Predict the main active ingredients, core targets, and related signaling pathways of Shenfu injection in the treatment of ischemic stroke. objective: Predicting the main active ingredients, core targets, and related signaling pathways of shenfu injection in the treatment of ischemic stroke. Methods: Databases were used to collect the active ingredients and target information of Shenfu injection; GO and KEGG pathway enrichment analyses were performed using the David database. The effects of Shenfu injection on core targets were verified using molecular docking and in vivo experiments. method: Databases were used to collect the active ingredients and target information of shenfu injection; GO and KEGG pathway enrichment analysis were performed using David database.The effects of shenfu injection on core targets were verified using molecular docking and in vivo experiments. Results: The predicted results identified 44 active ingredients and 635 targets in Shenfu injection, among which 418 targets, including TNF, IL-6, MAPK1, and MAPK14, were potential targets for the treatment of ischemic stroke. Molecular docking revealed that the active ingredients had good binding to IL-6, MAPK1, and MAPK14. In vivo experiments demonstrated that Shenfu injection significantly improved the pathological damage due to ischemic stroke, promoted the expression of tight junction proteins, and inhibited MMP-2 and MMP-9 expressions, thereby reducing BBB permeability. Animal experiments revealed that Shenfu injection could inhibit p38、JNK and ERK phosphorylation. Conclusions: Mechanism of Shenfu injection in treating ischemic stroke may be via inhibition of the inflammatory factors levels and protecting the BBB, thereby warranting subsequent studies and highlighting its potential as a reference for new drug development.
{"title":"Mechanism of Shenfu Injection in Treating Ischemic Stroke Elucidated using Network Pharmacology and Experimental Validation","authors":"Xuecheng Yu, Kun Shi, Bin Wu, Zengxiang Gao, Jiyuan Tu, Yan Cao, Linlin Chen, Guosheng Cao","doi":"10.2174/0115734099292513240404091734","DOIUrl":"https://doi.org/10.2174/0115734099292513240404091734","url":null,"abstract":"Background: Shenfu injection was derived from the classical Chinese medicine formula ‘Shenfu decoction’, which was widely used in the treatment of cardiovascular and cerebrovascular diseases in clinical practice. background: Shenfu injection is derived from the classical Chinese medicine formula ‘shenfu decoction’, which is widely used in the treatment of cardiovascular and cerebrovascular diseases in clinical practice. Objectives: Predict the main active ingredients, core targets, and related signaling pathways of Shenfu injection in the treatment of ischemic stroke. objective: Predicting the main active ingredients, core targets, and related signaling pathways of shenfu injection in the treatment of ischemic stroke. Methods: Databases were used to collect the active ingredients and target information of Shenfu injection; GO and KEGG pathway enrichment analyses were performed using the David database. The effects of Shenfu injection on core targets were verified using molecular docking and in vivo experiments. method: Databases were used to collect the active ingredients and target information of shenfu injection; GO and KEGG pathway enrichment analysis were performed using David database.The effects of shenfu injection on core targets were verified using molecular docking and in vivo experiments. Results: The predicted results identified 44 active ingredients and 635 targets in Shenfu injection, among which 418 targets, including TNF, IL-6, MAPK1, and MAPK14, were potential targets for the treatment of ischemic stroke. Molecular docking revealed that the active ingredients had good binding to IL-6, MAPK1, and MAPK14. In vivo experiments demonstrated that Shenfu injection significantly improved the pathological damage due to ischemic stroke, promoted the expression of tight junction proteins, and inhibited MMP-2 and MMP-9 expressions, thereby reducing BBB permeability. Animal experiments revealed that Shenfu injection could inhibit p38、JNK and ERK phosphorylation. Conclusions: Mechanism of Shenfu injection in treating ischemic stroke may be via inhibition of the inflammatory factors levels and protecting the BBB, thereby warranting subsequent studies and highlighting its potential as a reference for new drug development.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"38 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-13DOI: 10.2174/0115734099298932240308104437
Jiangwei Jia, Bo Liu, Xin Wang, Fenglan Ji, Fuchun Wen, Lianlian Song, Huibo Xu, Tao Ding
Background: Diabetic Retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. The Compound Qilian Tablets (CQLT) were developed in China for the treatment and prevention of DR, but their mechanism of action is still unclear. Objective: In the present study, network pharmacology, molecular docking, and in vivo validation experiments were used to investigate the active components and molecular mechanisms of CQLT against DR. Methods: The active components and targets of CQLT were collected through the TCSMP database, and the targets of DR were obtained from GeneCards, OMIM, and Drugbank databases. We established a protein-protein interaction network using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Metascape database. Molecular docking using AutoDock Vina was performed to investigate the interactions between components of CQLT and core targets. Moreover, we selected ZDF rats to establish a DR model for the experimental studies. Results: 39 active components and 448 targets in CQLT were screened, among which 90 targets were shared with DR. KEGG pathway enrichment analysis identified 181 pathways. The molecular docking results demonstrated that the main active components had strong binding ability to the core targets. The results from animal experiments indicate that the mechanism of CQLT against DR is associated with inhibiting the retinal mTOR/HIF-1α/VEGF signaling pathway, alleviating the inflammatory response, suppressing retinal neovascularization, and protecting the function and morphology of the retina. Conclusion: The present study preliminarily explored the mechanism of CQLT in treating DR and demonstrated that CQLT exerts anti-DR effects through multiple components, multiple targets, and multiple pathways. These findings suggest that CQLT shows promise as a potential therapeutic agent for DR and could contribute to developing novel treatments.
背景：糖尿病视网膜病变（DR）是糖尿病常见的慢性并发症之一，已发展成为全球成人不可逆视力损伤的主要原因。中国开发了复方芪连片（CQLT）用于治疗和预防 DR，但其作用机制仍不清楚。研究目的本研究采用网络药理学、分子对接和体内验证实验研究复方芪连片对 DR 的活性成分和分子机制。研究方法通过TCSMP数据库收集CQLT的活性成分和靶点，通过GeneCards、OMIM和Drugbank数据库获得DR的靶点。我们利用 STRING 数据库建立了蛋白质-蛋白质相互作用网络。使用 Metascape 数据库进行了基因本体（GO）和京都基因组百科全书（KEGG）通路富集分析。使用 AutoDock Vina 进行了分子对接，以研究 CQLT 成分与核心靶标之间的相互作用。此外，我们还选择了 ZDF 大鼠建立 DR 模型进行实验研究。结果筛选了 CQLT 中的 39 个活性成分和 448 个靶点，其中 90 个靶点与 DR 共享。KEGG 通路富集分析确定了 181 条通路。分子对接结果表明，主要活性成分与核心靶点有很强的结合能力。动物实验结果表明，CQLT 抗 DR 的机制与抑制视网膜 mTOR/HIF-1α/VEGF 信号通路、减轻炎症反应、抑制视网膜新生血管、保护视网膜功能和形态有关。结论本研究初步探讨了CQLT治疗DR的机制，证明CQLT通过多成分、多靶点、多途径发挥抗DR作用。这些研究结果表明，CQLT有望成为一种潜在的DR治疗药物，并有助于开发新型治疗方法。
{"title":"Network Pharmacology and Molecular Docking to Explore the Mechanism of Compound Qilian Tablets in Treating Diabetic Retinopathy","authors":"Jiangwei Jia, Bo Liu, Xin Wang, Fenglan Ji, Fuchun Wen, Lianlian Song, Huibo Xu, Tao Ding","doi":"10.2174/0115734099298932240308104437","DOIUrl":"https://doi.org/10.2174/0115734099298932240308104437","url":null,"abstract":"Background: Diabetic Retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. The Compound Qilian Tablets (CQLT) were developed in China for the treatment and prevention of DR, but their mechanism of action is still unclear. Objective: In the present study, network pharmacology, molecular docking, and in vivo validation experiments were used to investigate the active components and molecular mechanisms of CQLT against DR. Methods: The active components and targets of CQLT were collected through the TCSMP database, and the targets of DR were obtained from GeneCards, OMIM, and Drugbank databases. We established a protein-protein interaction network using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Metascape database. Molecular docking using AutoDock Vina was performed to investigate the interactions between components of CQLT and core targets. Moreover, we selected ZDF rats to establish a DR model for the experimental studies. Results: 39 active components and 448 targets in CQLT were screened, among which 90 targets were shared with DR. KEGG pathway enrichment analysis identified 181 pathways. The molecular docking results demonstrated that the main active components had strong binding ability to the core targets. The results from animal experiments indicate that the mechanism of CQLT against DR is associated with inhibiting the retinal mTOR/HIF-1α/VEGF signaling pathway, alleviating the inflammatory response, suppressing retinal neovascularization, and protecting the function and morphology of the retina. Conclusion: The present study preliminarily explored the mechanism of CQLT in treating DR and demonstrated that CQLT exerts anti-DR effects through multiple components, multiple targets, and multiple pathways. These findings suggest that CQLT shows promise as a potential therapeutic agent for DR and could contribute to developing novel treatments.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"33 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.2174/0115734099282407240325054745
De Kun Lu, Zheng Chang Guo, Jia Jia zhang, Xin Yu, Zong Yao Zhang
Background: Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear. Objective: This study aims to identify potential gene markers for inguinal hernia and available drugs. objective: This study aims to identify potential gene markers for inguinal hernia and available drugs. Methods: Pubmed2Ensembl text mining was used to identify genes related to "inguinal hernia" keywords. The GeneCodis system was used to specify GO biological process terms and KEGG pathways defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The STRING tool was used to construct protein-protein interaction networks, which were then visualized using Cytoscape.CytoHubba and Molecular Complex Detection were utilized to analyze the module (MCODE). A GO and KEGG analysis of gene modules was conducted using the DAVID platform database. Hub genes are those that are concentrated in prominent modules. The druggene interaction database was also used to identify potential drugs for inguinal hernia patients based on their interactions between the hub genes. Finally, a Mendelian randomization study was conducted based on genome-wide association studies to determine whether hub genes cause inguinal hernias. Results: The identification of 96 genes associated with inguinal hernia was carried out using text mining techniques. It was constructed using PPI networks with 80 nodes and 476 edges, and the sequence of the genes was performed using CytoHubba. MCODE analysis identified three gene modules. Three modules contain 37 genes clustered as hub candidate genes associated with inguinal hernia patients. The PI3K-Akt, MAPK, AGE-RAGE, and HIF-1 pathways were found to be enriched in signaling pathways. Sixteen of the 37 genes were found to be targetable by 30 existing drugs. The relationship between hub genes and inguinal hernia was examined using Mendelian randomization. The research revealed nine genes that may be connected with inguinal hernia, such as POMC, CD40LG, TFRC, VWF, LOX, IGF2, BRCA1, TNF, and HGF in the plasma. By inverse variance weighting, ALB was associated with an increased risk of inguinal hernia with an OR of 1.203 (OR [95%] = 1,04 [1.012 to 1.089], p = 0.008). Conclusion: We identified potential hub genes for inguinal hernia, predicted potential drugs for inguinal hernia, and reverse-validated potential genes by Mendelian randomization. This may provide further insights into asymptomatic pre-diagnostic methods and contribute to studies to understand the molecular mechanisms of risk genes associated with inguinal hernia.
{"title":"Functional Investigation and Two-sample Mendelian Randomization Study of Inguinal Hernia Hub Genes Obtained by Bioinformatics Analysis","authors":"De Kun Lu, Zheng Chang Guo, Jia Jia zhang, Xin Yu, Zong Yao Zhang","doi":"10.2174/0115734099282407240325054745","DOIUrl":"https://doi.org/10.2174/0115734099282407240325054745","url":null,"abstract":"Background: Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear. Objective: This study aims to identify potential gene markers for inguinal hernia and available drugs. objective: This study aims to identify potential gene markers for inguinal hernia and available drugs. Methods: Pubmed2Ensembl text mining was used to identify genes related to \"inguinal hernia\" keywords. The GeneCodis system was used to specify GO biological process terms and KEGG pathways defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The STRING tool was used to construct protein-protein interaction networks, which were then visualized using Cytoscape.CytoHubba and Molecular Complex Detection were utilized to analyze the module (MCODE). A GO and KEGG analysis of gene modules was conducted using the DAVID platform database. Hub genes are those that are concentrated in prominent modules. The druggene interaction database was also used to identify potential drugs for inguinal hernia patients based on their interactions between the hub genes. Finally, a Mendelian randomization study was conducted based on genome-wide association studies to determine whether hub genes cause inguinal hernias. Results: The identification of 96 genes associated with inguinal hernia was carried out using text mining techniques. It was constructed using PPI networks with 80 nodes and 476 edges, and the sequence of the genes was performed using CytoHubba. MCODE analysis identified three gene modules. Three modules contain 37 genes clustered as hub candidate genes associated with inguinal hernia patients. The PI3K-Akt, MAPK, AGE-RAGE, and HIF-1 pathways were found to be enriched in signaling pathways. Sixteen of the 37 genes were found to be targetable by 30 existing drugs. The relationship between hub genes and inguinal hernia was examined using Mendelian randomization. The research revealed nine genes that may be connected with inguinal hernia, such as POMC, CD40LG, TFRC, VWF, LOX, IGF2, BRCA1, TNF, and HGF in the plasma. By inverse variance weighting, ALB was associated with an increased risk of inguinal hernia with an OR of 1.203 (OR [95%] = 1,04 [1.012 to 1.089], p = 0.008). Conclusion: We identified potential hub genes for inguinal hernia, predicted potential drugs for inguinal hernia, and reverse-validated potential genes by Mendelian randomization. This may provide further insights into asymptomatic pre-diagnostic methods and contribute to studies to understand the molecular mechanisms of risk genes associated with inguinal hernia.","PeriodicalId":10886,"journal":{"name":"Current computer-aided drug design","volume":"62 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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