The Experts below are selected from a list of 52968 Experts worldwide ranked by ideXlab platform
Hye Won Chung - One of the best experts on this subject based on the ideXlab platform.
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serum ena78 cxcl5 sdf 1 cxcl12 and their combinations as potential biomarkers for prediction of the presence and distant metastasis of primary gastric cancer
Cytokine, 2015Co-Authors: Hye Won ChungAbstract:Abstract Background Chemokines play important roles in cancer development and progression. Epithelial-derived neutrophil-activating peptide-78 (ENA78/CXCL5) and stromal cell-derived factor (SDF-1/CXCL12) supposedly contribute to gastric cancer (GC) development and progression. This study aims to evaluate serum levels of ENA78/CXCL5 and SDF-1/CXCL12 along the GC carcinogenesis, and analyze their clinical significance, and diagnostic potentials through human serum samples. Methods A total of 300 subjects were enrolled in this study. Serum levels of ENA78/CXCL5 and SDF-1/CXCL12, measured by chemiluminescent immunoassay, were compared among 4 disease groups; normal, high-risk (intestinal metaplasia and adenoma), early GC (EGC), and advanced GC (AGC) groups in both training (n = 25 per group) and Validation Dataset (n = 70, 30, 50, 50, respectively) by ANOVA test (post hoc Bonferroni). Correlations between serum ENA78/CXCL5 or SDF-1/CXCL12 levels and clinicopathological parameters of GC patients were evaluated (Spearman’s correlation; γs). To validate the diagnostic accuracy, receiver operating characteristic (ROC) curve and logistic regression analysis was performed. Results Serum ENA78/CXCL5 and SDF-1/CXCL12 levels were significantly higher in AGC groups than EGC, high-risk and normal groups in both training and Validation Dataset (Bonferroni, from p Conclusions Combinations of initial serum ENA78/CXCL5, SDF-1/CXCL12, and CEA before any treatment for GC can produce valuable serum biomarker panels to predict the presence and distant metastasis of GC.
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serum ena78 cxcl5 sdf 1 cxcl12 and their combinations as potential biomarkers for prediction of the presence and distant metastasis of primary gastric cancer
Cytokine, 2015Co-Authors: Hye Won ChungAbstract:Abstract Background Chemokines play important roles in cancer development and progression. Epithelial-derived neutrophil-activating peptide-78 (ENA78/CXCL5) and stromal cell-derived factor (SDF-1/CXCL12) supposedly contribute to gastric cancer (GC) development and progression. This study aims to evaluate serum levels of ENA78/CXCL5 and SDF-1/CXCL12 along the GC carcinogenesis, and analyze their clinical significance, and diagnostic potentials through human serum samples. Methods A total of 300 subjects were enrolled in this study. Serum levels of ENA78/CXCL5 and SDF-1/CXCL12, measured by chemiluminescent immunoassay, were compared among 4 disease groups; normal, high-risk (intestinal metaplasia and adenoma), early GC (EGC), and advanced GC (AGC) groups in both training (n = 25 per group) and Validation Dataset (n = 70, 30, 50, 50, respectively) by ANOVA test (post hoc Bonferroni). Correlations between serum ENA78/CXCL5 or SDF-1/CXCL12 levels and clinicopathological parameters of GC patients were evaluated (Spearman’s correlation; γs). To validate the diagnostic accuracy, receiver operating characteristic (ROC) curve and logistic regression analysis was performed. Results Serum ENA78/CXCL5 and SDF-1/CXCL12 levels were significantly higher in AGC groups than EGC, high-risk and normal groups in both training and Validation Dataset (Bonferroni, from p Conclusions Combinations of initial serum ENA78/CXCL5, SDF-1/CXCL12, and CEA before any treatment for GC can produce valuable serum biomarker panels to predict the presence and distant metastasis of GC.
Masataka Sata - One of the best experts on this subject based on the ideXlab platform.
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deep learning for detection of elevated pulmonary artery wedge pressure using standard chest x ray
Canadian Journal of Cardiology, 2021Co-Authors: Yukina Hirata, Kenya Kusunose, Takumasa Tsuji, Kohei Fujimori, Junichi Kotoku, Masataka SataAbstract:Abstract Background To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest X-ray (CXR). Methods We enrolled 1,013 consecutive patients with a right heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with an elevated PAWP (> 18 mmHg) as the actual value of PAWP to be used in the Dataset for training. In the prospective Validation Dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. Results In the prospective Validation Dataset, the AUC of the DL model with CXR was not significantly different than the AUC produced by brain natriuretic peptide and the echocardiographic left ventricular diastolic dysfunction algorithm (DL model: 0.77 vs. BNP: 0.77 vs. DD algorithm: 0.70; respectively; p=NS for all comparisons), however was significantly higher than the AUC of the cardiothoracic ratio (DL model vs. CTR: 0.66, p=0.044). The model based on three parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; p=0.041). Conclusions Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.
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deep learning for detection of elevated pulmonary artery wedge pressure using standard chest x ray
Canadian Journal of Cardiology, 2021Co-Authors: Yukina Hirata, Kenya Kusunose, Takumasa Tsuji, Kohei Fujimori, Junichi Kotoku, Masataka SataAbstract:Abstract Background To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest x-ray (CXR). Methods We enrolled 1013 consecutive patients with a right-heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with elevated PAWP (> 18 mm Hg) as the actual value of PAWP to be used in the Dataset for training. In the prospective Validation Dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. Results In the prospective Validation Dataset, the AUC of the DL model with CXR was not significantly different from the AUC produced by brain natriuretic peptide (BNP) and the echocardiographic left-ventricular diastolic dysfunction (DD) algorithm (DL model: 0.77 vs BNP: 0.77 vs DD algorithm: 0.70; respectively; P = NS for all comparisons); it was, however, significantly higher than the AUC of the cardiothoracic ratio (DL model vs cardiothoracic ratio [CTR]: 0.66, P = 0.044). The model based on 3 parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; P = 0.041). Conclusions Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.
Gajendra P. S. Raghava - One of the best experts on this subject based on the ideXlab platform.
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In Silico Approach for Prediction of Antifungal Peptides
Frontiers Media S.A., 2018Co-Authors: Piyush Agrawal, Gajendra P. S. Raghava, Sherry Bhalla, Kumardeep Chaudhary, Rajesh Kumar, Meenu SharmaAbstract:This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training Dataset and 83.33% on independent or Validation Dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on Validation Dataset. We benchmark models developed in this study and existing methods on a Dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp)
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Data_Sheet_1.DOC
2018Co-Authors: Piyush Agrawal, Sherry Bhalla, Kumardeep Chaudhary, Rajesh Kumar, Meenu Sharma, Gajendra P. S. RaghavaAbstract:This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training Dataset and 83.33% on independent or Validation Dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on Validation Dataset. We benchmark models developed in this study and existing methods on a Dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp).
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QSAR-Based Models for Designing Quinazoline/ Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR
2016Co-Authors: Jagat Singh Chauhan, Deepak Singla, Open Source, Drug Discovery, Subhash M. Agarwal, Gajendra P. S. RaghavaAbstract:Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on Dataset containing 128 quinazoline based inhibitors. This Dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train Dataset while performance was evaluated on the wild_valid called Validation Dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on Validation Dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid Dataset and achieved a maximum correlation between 0.834 to 0.850 on these Datasets. Finally, an integrated hybrid model has been developed on a Dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different Datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalon
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QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR
PloS one, 2014Co-Authors: Jagat Singh Chauhan, Deepak Singla, Subhash M. Agarwal, Sandeep Kumar Dhanda, Gajendra P. S. RaghavaAbstract:Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on Dataset containing 128 quinazoline based inhibitors. This Dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train Dataset while performance was evaluated on the wild_valid called Validation Dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on Validation Dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid Dataset and achieved a maximum correlation between 0.834 to 0.850 on these Datasets. Finally, an integrated hybrid model has been developed on a Dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different Datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs.
Yukina Hirata - One of the best experts on this subject based on the ideXlab platform.
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deep learning for detection of elevated pulmonary artery wedge pressure using standard chest x ray
Canadian Journal of Cardiology, 2021Co-Authors: Yukina Hirata, Kenya Kusunose, Takumasa Tsuji, Kohei Fujimori, Junichi Kotoku, Masataka SataAbstract:Abstract Background To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest X-ray (CXR). Methods We enrolled 1,013 consecutive patients with a right heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with an elevated PAWP (> 18 mmHg) as the actual value of PAWP to be used in the Dataset for training. In the prospective Validation Dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. Results In the prospective Validation Dataset, the AUC of the DL model with CXR was not significantly different than the AUC produced by brain natriuretic peptide and the echocardiographic left ventricular diastolic dysfunction algorithm (DL model: 0.77 vs. BNP: 0.77 vs. DD algorithm: 0.70; respectively; p=NS for all comparisons), however was significantly higher than the AUC of the cardiothoracic ratio (DL model vs. CTR: 0.66, p=0.044). The model based on three parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; p=0.041). Conclusions Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.
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deep learning for detection of elevated pulmonary artery wedge pressure using standard chest x ray
Canadian Journal of Cardiology, 2021Co-Authors: Yukina Hirata, Kenya Kusunose, Takumasa Tsuji, Kohei Fujimori, Junichi Kotoku, Masataka SataAbstract:Abstract Background To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wedge pressure (PAWP). The aim of this study was to develop and validate an objective method for detecting elevated PAWP by applying deep learning (DL) to a chest x-ray (CXR). Methods We enrolled 1013 consecutive patients with a right-heart catheter between October 2009 and February 2020. We developed a convolutional neural network to identify patients with elevated PAWP (> 18 mm Hg) as the actual value of PAWP to be used in the Dataset for training. In the prospective Validation Dataset used to detect elevated PAWP, the area under the receiver operating characteristic curve (AUC) was calculated using the DL model that evaluated the CXR. Results In the prospective Validation Dataset, the AUC of the DL model with CXR was not significantly different from the AUC produced by brain natriuretic peptide (BNP) and the echocardiographic left-ventricular diastolic dysfunction (DD) algorithm (DL model: 0.77 vs BNP: 0.77 vs DD algorithm: 0.70; respectively; P = NS for all comparisons); it was, however, significantly higher than the AUC of the cardiothoracic ratio (DL model vs cardiothoracic ratio [CTR]: 0.66, P = 0.044). The model based on 3 parameters (BNP, DD algorithm, and CTR) was improved by adding the DL model (AUC: from 0.80 to 0.86; P = 0.041). Conclusions Applying the DL model based on a CXR (a classical, universal, and low-cost test) is useful for screening for elevated PAWP.
Nina Bhardwaj - One of the best experts on this subject based on the ideXlab platform.
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whole blood rna transcript based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor tremelimumab
Journal for ImmunoTherapy of Cancer, 2017Co-Authors: Philip Friedlander, John M Kirkwood, Karl Wassmann, Alan M Christenfeld, David E Fisher, Chrisann Kyi, Nina BhardwajAbstract:Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions. Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naive melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery Dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (Validation Dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the Validation Dataset. A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p < 0.0001) for objective response and 0.6 (95% confidence interval 0.54 to 0.67, p = 0.0066) for one-year survival in the discovery set. This model was validated in the Validation set with AUCs of 0.62 (95% confidence interval 0.54 to 0.70 p = 0.0455) for objective response and 0.68 for one-year survival (95% confidence interval 0.59 to 0.75 p = 0.0002). To our knowledge, this is the largest blood-based biomarker study of a checkpoint inhibitor, tremelimumab, which demonstrates a validated pre-treatment mRNA classifier model that predicts clinical response. The data suggest that the model captures a biological signature representative of genes needed for a robust anti-cancer immune response. It also identifies non-responders to tremelimumab at baseline prior to treatment.
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Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab
BMC, 2017Co-Authors: Philip Friedlander, John M Kirkwood, Karl Wassmann, Alan M Christenfeld, Chrisann Kyi, David Fisher, Nina BhardwajAbstract:Abstract Background Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions. Methods Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naïve melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery Dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (Validation Dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the Validation Dataset. Results A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p
