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Tan D, Jiang H, Li H, Xie Y, Su Y. Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism. Brief Funct Genomics 2024; 23:286-294. [PMID: 37642213 DOI: 10.1093/bfgp/elad037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 07/16/2023] [Accepted: 08/08/2023] [Indexed: 08/31/2023] Open
Abstract
The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction.
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Affiliation(s)
- Dayu Tan
- Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Hefei, China
| | - Haijun Jiang
- Key Laboratory of Intelligent Computing and Signal Processing, School of Computer Science and Technology, Anhui University, 111 Jiulong Road, 230601, Hefei, China
| | - Haitao Li
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Hefei, China
| | - Ying Xie
- School of Mechanical, Electrical and Information Engineering, Putian University, China
| | - Yansen Su
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Artificial Intelligence, Anhui University, 111 Jiulong Road, 230601, Hefei, China
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2
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Tan D, Su Y, Peng X, Chen H, Zheng C, Zhang X, Zhong W. Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism. IEEE Trans Cybern 2024; 54:2798-2810. [PMID: 37279140 DOI: 10.1109/tcyb.2023.3278110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.
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3
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Tan D, Huang Z, Peng X, Zhong W, Mahalec V. Deep Adaptive Fuzzy Clustering for Evolutionary Unsupervised Representation Learning. IEEE Trans Neural Netw Learn Syst 2024; 35:6103-6117. [PMID: 37027776 DOI: 10.1109/tnnls.2023.3243666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Cluster assignment of large and complex datasets is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the resampled data from estimated bottleneck space have consistent clustering properties to improve the deep clustering model progressively. Experiments on various datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.
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Wang R, Lin Y', Zhang C, Wu H, Jin Q, Guo J, Cao H, Tan D, Wu T. Fine mapping and analysis of a candidate gene controlling Phytophthora blight resistance in cucumber. Plant Biol (Stuttg) 2024. [PMID: 38607927 DOI: 10.1111/plb.13648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
Cucumber blight is a destructive disease. The best way to control this disease is resistance breeding. This study focuses on disease resistance gene mapping and molecular marker development. We used the resistant cucumber, JSH, and susceptible cucumber, B80, as parents to construct F2 populations. Bulked segregant analysis (BSA) combined with specific length amplified fragment sequencing (SLAF-seq) were used, from which we developed cleaved amplified polymorphic sequence (CAPs) markers to map the resistance gene. Resistance in F2 individuals showed a segregation ratio of resistance:susceptibility close to 3:1. The gene in JSH resistant cucumber was mapped to an interval of 9.25 kb, and sequencing results for the three genes in the mapped region revealed three mutations at base sites 225, 302, and 591 in the coding region of Csa5G139130 between JSH and B80, but no mutations in coding regions of Csa5G139140 and Csa5G139150. The mutations caused changes in amino acids 75 and 101 of the protein encoded by Csa5G139130, suggesting that Csa5G139130 is the most likely resistance candidate gene. We developed a molecular marker, CAPs-4, as a closely linked marker for the cucumber blight resistance gene. This is the first report on mapping of a cucumber blight resistance gene and will provideg a useful marker for molecular breeding of cucumber resistance to Phytophthora blight.
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Affiliation(s)
- R Wang
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - Y 'e Lin
- Vegetable Research Institute, Guangdong Academy of Agricultural Sciences/Guangdong Key Laboratory for New Technology Research of Vegetables, Guangzhou, China
| | - C Zhang
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - H Wu
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - Q Jin
- Vegetable Research Institute, Guangdong Academy of Agricultural Sciences/Guangdong Key Laboratory for New Technology Research of Vegetables, Guangzhou, China
| | - J Guo
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - H Cao
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - D Tan
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
| | - T Wu
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences (IFA, GDAAS), Guangzhou, China
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Kfoury M, Tan D. Specific issues in the systemic treatment strategy for ovarian clear cell carcinoma. ESMO Open 2024; 9:102568. [PMID: 38387110 PMCID: PMC10899029 DOI: 10.1016/j.esmoop.2024.102568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Affiliation(s)
- M Kfoury
- Medical Oncology Department, Institut Paoli-Calmettes, Marseille, France.
| | - D Tan
- Department of Medical Oncology, National University Cancer Institute, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Cao R, Zhang D, Wei P, Ding Y, Zheng C, Tan D, Zhou C. PMMNet: A Dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation. IEEE J Biomed Health Inform 2024; PP:1-12. [PMID: 38512745 DOI: 10.1109/jbhi.2024.3380054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are essential for their diagnosis and treatment. However, the majority of current research is focused on two-dimensional images, ignoring the 3D spatial information that is also critical. In this work, we propose a novel dual-branch fusion network called the Point Cloud and Multi-View Medical Neural Network (PMMNet) for IA classification and segmentation. Specifically, one branch based on 3D point clouds serves the purpose of extracting spatial features, whereas the other branch based on multi-view images acquires 2D pixel features. Ultimately, the two types of features are fused for IA classification and segmentation. To extract both local and global features from 3D point clouds, Multilayer Perceptron (MLP) and the attention mechanism are used in parallel. In addition, a SPSA module is proposed for multi-view image feature learning, which extracts more exquisite channel and spatial multi-scale features from 2D images. Experiments conducted on the IntrA dataset outperform other state-of-the-art methods, demonstrating that the proposed PMMNet exhibits strong superiority on the medical 3D dataset. We also obtain competitive results on public datasets, including ModelNet40, ModelNet10, and ShapeNetPart, which further validate the robustness and generality of the PMMNet.
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Tan D, Yang C, Wang J, Su Y, Zheng C. scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder. Brief Bioinform 2024; 25:bbae068. [PMID: 38426327 PMCID: PMC10905526 DOI: 10.1093/bib/bbae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/15/2024] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.
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Affiliation(s)
- Dayu Tan
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Cheng Yang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Jing Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
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Cao R, Ning L, Zhou C, Wei P, Ding Y, Tan D, Zheng C. CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images. Sensors (Basel) 2023; 23:8739. [PMID: 37960438 PMCID: PMC10650041 DOI: 10.3390/s23218739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/21/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023]
Abstract
Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network's ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications.
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Affiliation(s)
- Ruifen Cao
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, China; (R.C.); (L.N.)
| | - Long Ning
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, China; (R.C.); (L.N.)
| | - Chao Zhou
- Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, China;
| | - Pijing Wei
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China;
| | - Yun Ding
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China;
| | - Dayu Tan
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China;
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China;
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Tan D, Mohd Nasir NF, Abdul Manan H, Yahya N. Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review. Cancer Radiother 2023; 27:398-406. [PMID: 37482464 DOI: 10.1016/j.canrad.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity. MATERIALS AND METHODS A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria. RESULTS Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed. CONCLUSION Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.
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Affiliation(s)
- D Tan
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - N F Mohd Nasir
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia
| | - H Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia
| | - N Yahya
- Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia.
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Su Y, Lin R, Wang J, Tan D, Zheng C. Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data. Brief Bioinform 2023; 24:7008799. [PMID: 36715275 DOI: 10.1093/bib/bbad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/20/2022] [Accepted: 01/05/2023] [Indexed: 01/31/2023] Open
Abstract
A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.
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Affiliation(s)
- Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
| | - Rongxin Lin
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China
| | - Jing Wang
- School of Computer Science and Technology, Anhui University, Hefei, 230601, China
| | - Dayu Tan
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Chunhou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, 230601, China
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Chew N, Ng CH, Tan D, Kong G, Lin CX, Chin YH, Foo R, Chan M, Muthiah M. Global burden of metabolic diseases: data from Global Burden of Disease 2000-2019. A cosortium of metabolic disease. Eur Heart J 2023. [DOI: 10.1093/eurheartj/ehac779.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
The growing prevalence of metabolic diseases is a major concern. We sought to examine the global trends and mortality of metabolic diseases using estimates from the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) 2019.
Methods
Global estimates of prevalence, deaths, and disability-adjusted life year (DALYs) from 2000-2019 were examined for metabolic diseases (type 2 diabetes mellitus [T2DM], hypertension, and nonalcoholic fatty liver disease [NAFLD]). For metabolic risk factors (hyperlipidemia and obesity), estimates were limited to mortality and DALYs. Death rates was compared across sex, World Health Organisation regions and Socio-demographic Index (SDI) quintiles. Age-standardised prevalence and death rates were presented per 100,000 population with 95% uncertainty intervals (UI).
Findings
From 2000 to 2019, prevalence rates increased for all metabolic diseases, with the most pronounced increase in high SDI countries. In 2019, the mean (95%UI) age-standardised prevalence per 100,000 population was estimated to be 15,023 (13,493-16,764) for NAFLD, 5,283 (4,864–5,720) for T2DM and 234 (171-313) for hypertension. The highest age-standardised death rates were observed in obesity (62·59 [39·92-89·13]; males, 66·55 [39·76-97·21]; females. 58·14 [38·53-81·39]), followed by hyperlipidemia (56·51 [41·83-73·62]; males, 67·33 [50·78-86·43]; females, 46·50 [32·70-62·38]), T2DM (18·49 [17·18-19·66], males, 67·33 [50·78-86·43]; females, 46·50 [32·70-62·38]), hypertension (15·16 [11·20-16·75]; males, 14·95 [10·32-16·75]; females, 15·05 [11·51-17·09]) and NAFLD (2·09 [1·61-2·60]; males, 2·38 [1·82-3·02]; females, 1·82 [1·41-2·27]). Mortality rates decreased over time in hyperlipidemia (-154%), hypertension (-52%) and NAFLD (-52%), but not in T2DM and obesity. The highest mortality for metabolic diseases was found in Eastern Mediterranean, and low to low-middle SDI countries.
Conclusion
The global prevalence of metabolic diseases has risen over the past two decades regardless of SDI. Attention is needed to address the unchanging mortality rates attributed to metabolic disease and the regional, socioeconomic, and sex disparities in mortality from metabolic disease.
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Affiliation(s)
- N Chew
- National University Health System , Singapore , Singapore
| | - C H Ng
- National University Health System , Singapore , Singapore
| | - D Tan
- National University Health System , Singapore , Singapore
| | - G Kong
- National University Heart Centre , Singapore , Singapore
| | - C X Lin
- National University Heart Centre , Singapore , Singapore
| | - Y H Chin
- National University Heart Centre , Singapore , Singapore
| | - R Foo
- National University Heart Centre , Singapore , Singapore
| | - M Chan
- National University Heart Centre , Singapore , Singapore
| | - M Muthiah
- National University Health System , Singapore , Singapore
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Chew NWS, Ng CH, Kong G, Tan D, Lim WH, Kofidis T, Yip J, Loh PH, Chan KH, Low A, Lee CH, Yeo TC, Tan HC, Chan MY. Reconstructed meta-analysis of percutaneous coronary intervention versus coronary artery bypass grafting for left main disease. Eur Heart J 2022. [DOI: 10.1093/eurheartj/ehac544.1415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Background
Randomized controlled trials (RCTs) comparing percutaneous coronary intervention (PCI) with drug-eluting stents and coronary artery bypass grafting (CABG) for patients with left main coronary artery disease (LMCAD) have reported conflicting results.
Objectives
We performed a systematic review from inception to 23 May 2021 and one-stage reconstructed individual-patient data meta-analysis (IPDMA) that included 10-year mortality outcomes.
Methods
The primary outcome was 10-year all-cause mortality. Secondary outcomes included myocardial infarction (MI), stroke and unplanned revascularization at 5 years. We did IPDMA using published Kaplan-Meier curves to provide individual data points in coordinates and numbers at risk were used to increase the calibration accuracy of the reconstructed data. Shared frailty model or, when proportionality assumptions were not met, a restricted mean survival time model were fitted to compare outcomes between treatment groups.
Results
Of 583 articles retrieved, 5 RCTs were included. A total of 4595 patients from these 5 RCTs were randomly assigned to PCI (N=2297) or CABG (N=2298). The cumulative 10-year all-cause mortality after PCI and CABG was 12.0% versus 10.6% respectively (HR 1.093, 95% CI: 0.925–1.292; p=0.296). PCI conferred similar time-to-MI (RMST ratio 1.006, 95% CI: 0.992–1.021, p=0.391) and stroke (RMST ratio 1.005, 95% CI: 0.998–1.013, p=0.133) at 5 years. Unplanned revascularization was more frequent following PCI compared with CABG (HR 1.807, 95% CI: 1.524–2.144, p<0.001) at 5 years.
Conclusion
This meta-analysis using reconstructed participant-level time-to-event data showed no statistically significant difference in cumulative 10-year all-cause mortality between PCI versus CABG in the treatment of LMCAD.
Funding Acknowledgement
Type of funding sources: None.
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Affiliation(s)
- N W S Chew
- National University Heart Centre , Singapore , Singapore
| | - C H Ng
- National University of Singapore , Singapore , Singapore
| | - G Kong
- National University of Singapore , Singapore , Singapore
| | - D Tan
- National University of Singapore , Singapore , Singapore
| | - W H Lim
- National University of Singapore , Singapore , Singapore
| | - T Kofidis
- National University Heart Centre , Singapore , Singapore
| | - J Yip
- National University Heart Centre , Singapore , Singapore
| | - P H Loh
- National University Heart Centre , Singapore , Singapore
| | - K H Chan
- National University Heart Centre , Singapore , Singapore
| | - A Low
- National University Heart Centre , Singapore , Singapore
| | - C H Lee
- National University Heart Centre , Singapore , Singapore
| | - T C Yeo
- National University Heart Centre , Singapore , Singapore
| | - H C Tan
- National University Heart Centre , Singapore , Singapore
| | - M Y Chan
- National University Heart Centre , Singapore , Singapore
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Li S, Liu S, Wu Y, Liu Y, Tan D, Fan Y, Wei C, Xiong H. VP.21 Baseline nutrition investigation in a Chinese cohort of pediatric patients with spinal muscular atrophy. Neuromuscul Disord 2022. [DOI: 10.1016/j.nmd.2022.07.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Tan D, Zhang H, Xiong H. VP.77 Muscle transcriptomic study of a novel LAMA2-related congenital muscular dystrophy mouse model. Neuromuscul Disord 2022. [DOI: 10.1016/j.nmd.2022.07.339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Huang X, Yang H, Tan D, Ge L, Fan Y, Chang X, Yang Z, Xiong H. VP.78 Clinical and genetic study of LAMA2-related muscular dystrophy patients with seizures. Neuromuscul Disord 2022. [DOI: 10.1016/j.nmd.2022.07.340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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16
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Geng P, Ling B, Yang Y, Walline JH, Song Y, Lu M, Wang H, Zhu Q, Tan D, Xu J. THIRD bedside ultrasound protocol for rapid diagnosis of undifferentiated shock: a prospective observational study. Hong Kong Med J 2022; 28:383-391. [PMID: 36171145 DOI: 10.12809/hkmj219648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
INTRODUCTION It is clinically challenging to differentiate the pathophysiological types of shock in emergency situations. Here, we evaluated the ability of a novel bedside ultrasound protocol (Tamponade/tension pneumothorax, Heart, Inferior vena cava, Respiratory system, Deep venous thrombosis/aorta dissection [THIRD]) to predict types of shock in the emergency department. METHODS An emergency physician performed the THIRD protocol on all patients with shock who were admitted to the emergency department. All patients were closely followed to determine their final clinical diagnoses. The kappa index, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the initial diagnostic impression provided by the THIRD protocol, compared with each patient's final diagnosis. RESULTS In total, 112 patients were enrolled in this study. The kappa index between initial impression and final diagnosis was 0.81 (95% confidence interval=0.73-0.89; P<0.001). For hypovolaemic, cardiogenic, distributive, and obstructive types of shock, the sensitivities of the THIRD protocol were 100%, 100%, 93%, and 100%, respectively; the sensitivity for a 'mixed' shock aetiology was 86%. The negative predictive value of the THIRD protocol for all five types of shock was ≥96%. CONCLUSION Initial diagnostic judgements determined using the THIRD protocol showed favourable agreement with the final diagnosis in patients who presented with undifferentiated shock. The THIRD protocol has great potential for use as a bedside approach that can guide the rapid management of undifferentiated shock in emergency settings, particularly for patients with obstructive, hypovolaemic, or cardiogenic shock.
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Affiliation(s)
- P Geng
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - B Ling
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Y Yang
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - J H Walline
- Accident and Emergency Medicine Academic Unit, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong
| | - Y Song
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - M Lu
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - H Wang
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - Q Zhu
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - D Tan
- Department of Emergency Medicine, Clinical Medical College of Yangzhou University, Northern Jiangsu People's Hospital, Yangzhou, China
| | - J Xu
- Department of Emergency Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
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Sukhadia B, Tan D, Oh Y, Chae Y. EP08.02-023 Differentiation Syndrome in a Patient with Non-Small-Cell Lung Cancer Harboring IDH2 Mutation Treated with Enasidenib. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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18
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Ang T, Tan D, Goh B, Ng WT, Tan BBC, See B. Functional assessment of military aircrew applicants in a hypobaric chamber. Occup Med (Lond) 2022; 72:452-455. [DOI: 10.1093/occmed/kqac059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Aircrew are exposed to environmental pressure changes. In the Republic of Singapore Air Force (RSAF), applicants assessed to be at intermediate risk of otic barotrauma undergo a hypobaric chamber assessment [“trial of chamber” (TOC)] to functionally evaluate their suitability for military aircrew vocations.
Aims
To identify factors associated with TOC failure among applicants with otorhinolaryngological conditions.
Methods
All applicants to RSAF aircrew vocations who were assessed to be at intermediate risk of otic barotrauma over a 3-yr period were identified using the RSAF Aeromedical Centre’s electronic database. Their medical records, as well as the TOC assessment records of the subset of applicants who underwent TOC, were reviewed for demographic data, clinical findings, and TOC outcomes.
Results
Of the 483 identified applicants, 374 (77%) had abnormal otoscopic findings, 103 (21%) had rhinitis symptoms, and 6 (1%) had previous ENT surgery. 123 (25%) underwent TOC, of which 20 (16%) failed. Holding other predictor variables constant, the odds of TOC failure increased by 0.79 per unit decrease in BMI (95% CI 0.63–0.99), and the odds of TOC failure increased by 0.93 per kg decrease in body weight (95% CI 0.87–1.00). An abnormal tympanogram was not a statistically significant predictor of TOC failure (OR 1.96, 95% CI 0.59–6.42). Of the 47 applicants who passed TOC and were eventually recruited, none subsequently developed otic barotrauma (mean follow-up, 3.3 yr ± 1.5 yr).
Conclusions
Applicants with lower weight and BMI are more likely to develop otic barotrauma with environmental pressure change. Tympanometry cannot be reliably used to identify applicants who would more likely pass TOC.
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Affiliation(s)
- T Ang
- Aeromedical Centre, Republic of Singapore Air Force Medical Service , Singapore 539945
| | - D Tan
- Aeromedical Centre, Republic of Singapore Air Force Medical Service , Singapore 539945
| | - B Goh
- Aeromedical Centre, Republic of Singapore Air Force Medical Service , Singapore 539945
| | - W T Ng
- Saw Swee Hock School of Public Health, National University of Singapore , Singapore 117549
| | - B B C Tan
- Aeromedical Centre, Republic of Singapore Air Force Medical Service , Singapore 539945
| | - B See
- Aeromedical Centre, Republic of Singapore Air Force Medical Service , Singapore 539945
- Saw Swee Hock School of Public Health, National University of Singapore , Singapore 117549
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Wang Q, He W, Zino L, Tan D, Zhong W. Bipartite consensus for a class of nonlinear multi-agent systems under switching topologies: A disturbance observer-based approach. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.02.081] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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20
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Ward J, Gill S, Armstrong K, Fogarty T, Tan D, Scott A, Yahya A, Dhaliwal S, Jacques A, Tang C. PO-1384 Simethicone use to Reduce Rectal Variability During Prostate Cancer Radiotherapy, a Randomised Trial. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)03348-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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21
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Tan D, Zhong W, Peng X, Wang Q, Mahalec V. Accurate and Fast Deep Evolutionary Networks Structured Representation Through Activating and Freezing Dense Networks. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2020.3017100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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22
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Wei PJ, Pang ZZ, Jiang LJ, Tan D, Su Y, Zheng CH. Promoter Prediction in Nannochloropsis Based on Densely Connected Convolutional Neural Networks. Methods 2022; 204:38-46. [DOI: 10.1016/j.ymeth.2022.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/03/2022] [Accepted: 03/28/2022] [Indexed: 10/18/2022] Open
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Wang J, Xia J, Tan D, Lin R, Su Y, Zheng CH. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation. Brief Bioinform 2022; 23:6523126. [PMID: 35136924 DOI: 10.1093/bib/bbab588] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/08/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https://github.com/WJ319/scHFC.
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Affiliation(s)
- Jing Wang
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Junfeng Xia
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Dayu Tan
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
| | - Rongxin Lin
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China
| | - Yansen Su
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Chun-Hou Zheng
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
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Asokkumar R, Seow I, Chin Hong L, Chang J, Tan D, Salazar E. Rostered routine testing for severe acute respiratory coronavirus virus 2 infection among healthcare workers: Do we detect more? J Gastroenterol Hepatol 2022; 37:404-405. [PMID: 34694645 PMCID: PMC8656364 DOI: 10.1111/jgh.15720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 10/20/2021] [Indexed: 12/13/2022]
Affiliation(s)
- R Asokkumar
- Department of Gastroenterology and HepatologySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
| | - I Seow
- Department of Colorectal SurgerySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
| | - L Chin Hong
- Department of Upper Gastrointestinal and Bariatric SurgerySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
| | - J Chang
- Department of Gastroenterology and HepatologySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
| | - D Tan
- Department of Gastroenterology and HepatologySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
| | - E Salazar
- Department of Gastroenterology and HepatologySingapore General HospitalSingapore,Duke‐NUS Graduate Medical SchoolSingapore
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25
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Tan A, Lai G, Saw S, Chua K, Takano A, Ong B, Koh T, Jain A, Tan W, Ng Q, Kanesvaran R, Rajasekaran T, Kalshnikova E, Shchegrova S, H. -Ta, Lin J, Renner D, Sethi H, Zimmermann B, Aleshin A, Lim W, Tan E, Skanderup A, Ang M, Tan D. MA07.06 Circulating Tumor DNA for Monitoring Minimal Residual Disease and Early Detection of Recurrence in Early Stage Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Dooms C, Nadal E, Raskin J, Demedts I, Mazieres J, Wislez M, Abdul S, Mun T, Wang C, Viteri S, Le X, How S, Tan D, Takeda M, Veillon R, Karachaliou N, Ellers-Lenz B, Smit E, Wu Y. P47.09 Tepotinib + Osimertinib for EGFR-Mutant NSCLC with Resistance to First-Line Osimertinib Due to MET amplification: INSIGHT 2. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Drilon A, Lin J, Lassen U, Leyvraz S, Liu Y, Patel J, Rosen L, Solomon B, Norenberg R, Dima L, Brega N, Shen L, Moreno V, Kummar S, Tan D. P53.02 Efficacy and Safety of Larotrectinib in Patients With Tropomyosin Receptor Kinase (TRK) Fusion-Positive Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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Li F, Zhang F, Tan D, Ye J, Tong W. Robotic transanal total mesorectal excision combined with intersphincteric resection for ultra-low rectal cancer. Tech Coloproctol 2021; 25:1335-1336. [PMID: 34236533 DOI: 10.1007/s10151-021-02494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 06/23/2021] [Indexed: 11/24/2022]
Affiliation(s)
- F Li
- Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - F Zhang
- Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - D Tan
- Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China
| | - J Ye
- Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China.,Department of General Surgery, The People's Hospital of Shapingba District, Chongqing, China
| | - W Tong
- Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China.
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29
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De Mel S, Goh J, Rashid MBMA, Zhang XY, Jaynes P, Liu X, Poon L, Chan E, Lee J, Chee YL, Koh LP, Tan LK, Soh TG, Yuen YC, Loi H, Ng S, Goh X, Tan D, Cheah DMZ, Pang WL, Huang D, Chan JY, Somasundaram N, Tang T, Lim ST, Ong CK, Chng W, Chow EK, Jeyasekharan AD. CLINICAL APPLICATION OF AN EX‐VIVO PLATFORM TO GUIDE THE CHOICE OF DRUG COMBINATIONS IN RELAPSED/REFRACTORY LYMPHOMA; A PROSPECTIVE STUDY. Hematol Oncol 2021. [DOI: 10.1002/hon.147_2880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- S De Mel
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - J Goh
- National University of Singapore Cancer Science Institute of Singapore Singapore Singapore
| | | | - X. Y Zhang
- National University of Singapore Cancer Science Institute of Singapore Singapore Singapore
| | - P Jaynes
- National University of Singapore Cancer Science Institute of Singapore Singapore Singapore
| | - X Liu
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - L Poon
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - E Chan
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - J Lee
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - Y. L Chee
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - L. P Koh
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - L. K Tan
- National University Hospital Department of Laboratory Medicine Singapore Singapore
| | - T. G Soh
- National University Hospital Department of Laboratory Medicine Singapore Singapore
| | - Y. C Yuen
- National University Health System Department of Pharmacy Singapore Singapore
| | - Hoi‐Y Loi
- National University Hospital Singapore Department of Diagnostic Imaging Singapore Singapore
| | - Siok‐B Ng
- National University of Singapore Department of Pathology Yong Loo Lin School of Medicine Singapore Singapore
| | - X Goh
- National University Hospital, Department of Otorhinolaryngology Singapore Singapore
| | - D Tan
- Mt Elizabeth Hospital, Dr Daryl Tan Clinic for Lymphoma, Myeloma and Blood Disorders Singapore Singapore
| | - D. M. Z Cheah
- National Cancer Centre Singapore Lymphoma Genomic Translational Research Laboratory Division of Cellular and Molecular Research Singapore Singapore
| | - W. L Pang
- National Cancer Centre Singapore Lymphoma Genomic Translational Research Laboratory Division of Cellular and Molecular Research Singapore Singapore
| | - D Huang
- National Cancer Centre Singapore Lymphoma Genomic Translational Research Laboratory Division of Cellular and Molecular Research Singapore Singapore
| | - J. Y Chan
- National Cancer Centre Singapore Division of Medical Oncology Singapore Singapore
| | - N Somasundaram
- National Cancer Centre Singapore Division of Medical Oncology Singapore Singapore
| | - T Tang
- National Cancer Centre Singapore Division of Medical Oncology Singapore Singapore
| | - S. T Lim
- National Cancer Centre Singapore Division of Medical Oncology Singapore Singapore
| | - C. K Ong
- National Cancer Centre Singapore Division of Cellular and Molecular Research Singapore Singapore
| | - W.‐J Chng
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
| | - E. K Chow
- National University of Singapore Cancer Science Institute of Singapore Singapore Singapore
| | - A. D Jeyasekharan
- National University Health System Department of Haematology‐Oncology National University Cancer Institute, Singapore Singapore Singapore
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Lim K, Tan G, Tan D, Tan A, Chen E. P37.18 Lung NSCLC Molecular Diagnostic Comparison Between NGS and Multiplex PCR Assays. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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31
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Tan W, Chua B, Yin D, Tan S, Tan D, Ang M, Kanesvaran R, Jain A, Rajasekaran T, Lai G, Toh C, Tan E, Ng Q, Lim W. P76.46 First-Line Osimertinib in Asian Patients with Advanced EGFR-Mutant Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.1103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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32
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Yuan J, Lim E, Ma S, Chua K, Lee Y, Lim M, Yeo X, Phua C, Takano A, Aung Z, Lim K, Tan E, Tan D, Chew G, Tam W. P69.05 Molecular and Cellular Heterogeneity Underpin Treatment Response Across a Spectrum of EGFR-Mutant Non-Small Cell Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.1011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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33
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Tan A, Lai G, Tan G, Seet A, Takano A, Alvarez J, Skanderup A, Tan W, Ang M, Kanesvaran R, Ng Q, Jain A, Rajasekaran T, Lim W, Tan E, Lim K, Tan D. FP14.13 Molecular Characterisation and Clinical Outcomes in RET Rearranged Non-Small Cell Lung Cancer (NSCLC). J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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34
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Tan A, Ong B, Koh T, Chen J, Oo H, Lai G, Tan W, Ang M, Kanesvaran R, Ng Q, Jain A, Rajasekaran T, Zhai W, Skanderup A, Lim K, Tan E, Lim W, Tan D, Takano A. P38.03 Immunohistochemical, Histologic and Genomic Characterisation of Early Stage Pulmonary Invasive Mucinous Adenocarcinoma. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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35
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Felip E, Minotti V, Tan D, Wolf J, Mark M, Boyer M, Hughes B, Bearz A, Moro-Sibilot D, Le X, Vazquez J, Massuti B, Liu N, Hao L, Cheng Y, Tiedt R, Cobo M. P76.03 Efficacy and Safety of Capmatinib Plus Nivolumab in Pretreated Patients with EGFR Wild-Type Non–Small Cell Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.1060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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36
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Ma J, Tan S, Yin D, Tran A, Tan D, Ang M, Takano A, Lim K, Kanesvaran R, Jain A, Rajasekaran T, Tan E, Lim D, Ng Q, Tan W. P76.88 Real-World Data of Osimertinib in Patients with Metastatic EGFRm+ NSCLC who Progressed on First-Line EGFR TKIs. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.1145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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37
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Loong H, Goto K, Park K, Ohe Y, Nishio M, Cho B, Kim Y, French P, Soldatenkova V, Tan D. FP14.10 Efficacy and Safety of Selpercatinib (LOXO-292) in East Asian Patients with RET Fusion-Positive NSCLC. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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38
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Lai G, Alvarez J, Yeo J, Sim N, Tan A, Zhou S, Suteja L, Lim T, Rohatgi N, Yeong J, Takano A, Lim K, Gogna A, Too C, Zhuang K, Jain A, Tan W, Kanesvaran R, Ng Q, Ang M, Rajasekaran T, Wang L, Toh C, Lim W, Tam W, Ginhoux F, Tan S, Skanderup A, Tan D, Tan E. OA01.06 Randomised Phase 2 Study of Nivolumab (N) Versus Nivolumab and Ipilimumab (NI) Combination in EGFR Mutant NSCLC. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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39
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Saw S, Lai G, Zhou S, Chen J, Ang M, Chua K, Kanesvaran R, Ng Q, Jain A, Tan W, Rajasekaran T, Lim D, Fong K, Takano A, Cheng X, Lim K, Koh T, Ong B, Tan E, Skanderup A, Tan D. OA06.05 Molecular and Clinical Features Associated with Relapse in Early Stage EGFR-Mutated NSCLC: A Single Institution Knowledge Bank. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Tan A, Chua K, Teng Y, Takano A, Alvarez J, Nahar R, Rohatgi N, Lai G, Aung Z, Yeong J, Lim K, Naeini M, Kassam I, Jain A, Tan W, Gogna A, Too C, Kanesvaran R, Ng Q, Ang M, Rajasekaran T, Devanand A, Phua G, Tan B, Lee Y, Wang L, Teo A, Khng A, Lim M, Suteja L, Toh C, Lim W, Iyer N, Tam W, Tan E, Zhai W, Hillmer A, Skanderup A, Tan D. MA13.08 Genomic and Transcriptomic Features of Distinct Resistance Trajectories in EGFR Mutant Non-Small Cell Lung Cancer (NSCLC). J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Alvarez J, Chua K, Sim N, Abedi M, Chen J, Tan A, Lai G, Takano A, Lim W, Tan E, Lim K, Zhai W, Tan D, Skanderup A. P59.08 THOR: Multi-Ethnic, Open Access Thoracic Cancer Genomics Resource. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lee J, Tan A, Zhou S, Liu S, Kim D, Masuda K, Batra U, Hayashi H, Goto Y, Tan S, Wu Y, Tan D, Ahn M. MA04.06 Clinical Characteristics and Outcomes in Advanced KRAS Mutant NSCLC – A Multi-Centre Collaboration in Asia (ATORG-005). J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Tan D, Farago A, Kummar S, Moreno V, Patel J, Lassen U, Solomon B, Rosen L, Leyvraz S, Reeves J, Brega N, Dima L, Childs B, Drilon A. MA11.09 Efficacy and Safety of Larotrectinib in Patients with Tropomyosin Receptor Kinase (TRK) Fusion Lung Cancer. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Abstract
Despite the amazing progress in the treatment of gastric cancer (GC), it is still the third leading cause of cancer death in the world. This study explored the key genes that are related to the prognosis and pathogenesis of GC. Data from the cancer genome atlas (TCGA) and Oncomine were applied to evaluate the expression of cystatin 2 (CST2) in GC samples. Kaplan-Meier plotter was carried out to detect the overall survival of GC patients with different expression levels of CST2. Gene Set Enrichment Analysis (GSEA) was carried out to investigate the functions and pathways connected with CST2 expression. Quantitative real-time polymerase chain reaction (qPCR) and Western blot assays were used to assess CST2 expression. The biological properties of GC cells were assessed with the support of cell proliferation and Transwell assays. Important proteins involved in the regulation of CST2 in GC cell behaviors were evaluated by Western blot. Through analysis of the database, we found that CST2 expression was significantly upregulated in GC samples and actively related to GC patients' poor outcomes. Importantly, the analysis of GSEA showed that GST2 expression was closely connected with the proliferation and migration of cells, as well as the TGF-β1 signaling pathway. In addition, biological assays illustrated that over-expression of CST2 strengthened the activity and metastasis of GC cells. After the upregulation of CST2, the expression of cyclin D1, N-cadherin, vimentin, TGF-β1, and Smad4 increased, and E-cadherin expression decreased. Our findings revealed that over-expression of CST2 enhanced the growth, migration, and invasion of GC cells through modulating the epithelial-mesenchymal transition (EMT) and TGF-β1 signaling pathway, affording a possible biomarker for the treatment of GC.
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Affiliation(s)
- W P Zhang
- Department of General Surgery, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, P. R. China
| | - Y Wang
- Department of General Surgery, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, P. R. China
| | - D Tan
- Department of General Surgery, Ruijin Hospital/Lu Wan Branch, School of Medicine, Shanghai Jiaotong University, Shanghai, P. R. China
| | - C G Xing
- Department of General Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, P. R. China
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Tsoi K, Tan D, Stevenson J, Evans S, Jeys L, Botchu R. Indeterminate pulmonary nodules are not associated with worse overall survival in Ewing Sarcoma. J Clin Orthop Trauma 2021; 16:58-64. [PMID: 33717939 PMCID: PMC7920159 DOI: 10.1016/j.jcot.2020.12.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/26/2020] [Accepted: 12/16/2020] [Indexed: 11/26/2022] Open
Abstract
AIM Lung metastases are a negative prognostic factor in Ewing sarcoma, however, the incidence and significance of sub-centimetre pulmonary nodules at diagnosis is unclear. The aims of this study were to (1): determine the incidence of indeterminate pulmonary nodules (IPNs) in patients diagnosed with Ewing sarcoma (2); establish the impact of IPNs on overall and metastasis-free survival and (3) identify patient, oncological and radiological factors that correlate with poorer prognosis in patients that present with IPNs on their staging chest CT. MATERIALS & METHODS Between 2008 and 2016, 173 patients with a first presentation of Ewing sarcoma of bone were retrospectively identified from an institutional database. Staging and follow-up chest CTs for all patients with IPN were reviewed by a senior radiologist. Clinical and radiologic course were examined to determine overall- and metastasis-free survival for IPN patients and to identify demographic, oncological or nodule-specific features that predict which IPN represent true lung metastases. RESULTS Following radiologic re-review, IPN were found in 8.7% of patients. Overall survival for IPN patients was comparable to those with a normal staging chest CT (2-year overall survival of 73.3% [95% CI 43.6-89] and 89.4% [95% CI 81.6-94], respectively; p = 0.34) and was significantly better than for patients with clear metastases (46.0% [95% CI 31.9-59]; p < 0.0001). Similarly, there was no difference in metastasis-free survival between 'No Metastases' and 'IPN' patients (p = 0.16). Lung metastases developed in 40% of IPN patients at a median 9.6 months. Reduction of nodule size on neoadjuvant chemotherapy was associated with worse overall survival in IPN patients (p = 0.0084). CONCLUSION IPN are not uncommon in patients diagnosed with Ewing sarcoma. In this study, we were unable to detect a difference in overall- or metastasis-free survival between patients with IPN at diagnosis and patients with normal staging chest CTs.
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Affiliation(s)
- K.M. Tsoi
- Oncology Department, Royal Orthopaedic Hospital, Birmingham, UK
| | - D. Tan
- Oncology Department, Royal Orthopaedic Hospital, Birmingham, UK
| | - J. Stevenson
- Oncology Department, Royal Orthopaedic Hospital, Birmingham, UK,Aston University Medical School, Birmingham, UK
| | - S. Evans
- Oncology Department, Royal Orthopaedic Hospital, Birmingham, UK
| | - L.M. Jeys
- Oncology Department, Royal Orthopaedic Hospital, Birmingham, UK,Aston University Medical School, Birmingham, UK
| | - R. Botchu
- Department of Musculoskeletal Radiology, Royal Orthopaedic Hospital, Birmingham, UK,Corresponding author. Department of Musculoskeletal Radiology, The Royal Orthopedic Hospital, Bristol Road South, Northfield, Birmingham, UK.
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Li Y, Du X, Liu S, Tan D, Li Z, Li L, Miao F. PNS14 Investigation of Physicians’ Digital Activities on Mitigating the IMPACT of Covid-19 in China. Value Health Reg Issues 2020. [PMCID: PMC7487513 DOI: 10.1016/j.vhri.2020.07.433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Tan D. REVIEW CHAPTER ON IDEAL STUDENT LEADERSHIP AND THE IMPACT ON YOUTH DEVELOPMENT: LEADERSHIP AND PSYCHOLOGY FROM A PRACTITIONER’S PERSPECTIVE. Leadership 2020. [DOI: 10.1142/9789811213236_0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- D. Tan
- Singapore Chinese Chamber of Commerce & Industry, Singapore
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Naghiloo M, Tan D, Harrington PM, Alonso JJ, Lutz E, Romito A, Murch KW. Heat and Work Along Individual Trajectories of a Quantum Bit. Phys Rev Lett 2020; 124:110604. [PMID: 32242716 DOI: 10.1103/physrevlett.124.110604] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 02/07/2020] [Indexed: 06/11/2023]
Abstract
We use a near quantum limited detector to experimentally track individual quantum state trajectories of a driven qubit formed by the hybridization of a waveguide cavity and a transmon circuit. For each measured quantum coherent trajectory, we separately identify energy changes of the qubit as heat and work, and verify the first law of thermodynamics for an open quantum system. We further establish the consistency of these results by comparison with the master equation approach and the two-projective-measurement scheme, both for open and closed dynamics, with the help of a quantum feedback loop that compensates for the exchanged heat and effectively isolates the qubit.
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Affiliation(s)
- M Naghiloo
- Department of Physics, Washington University, St. Louis, Missouri 63130, USA
| | - D Tan
- Department of Physics, Washington University, St. Louis, Missouri 63130, USA
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
| | - P M Harrington
- Department of Physics, Washington University, St. Louis, Missouri 63130, USA
| | - J J Alonso
- Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, D-91058 Erlangen, Germany
| | - E Lutz
- Institute for Theoretical Physics I, University of Stuttgart, D-70550 Stuttgart, Germany
| | - A Romito
- Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom
| | - K W Murch
- Department of Physics, Washington University, St. Louis, Missouri 63130, USA
- Institute for Materials Science and Engineering, St. Louis, Missouri 63130, USA
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Mountzios G, Remon J, Novello S, Blais N, Califano R, Cufer T, Dingemans AM, Liu SV, Peled N, Pennell NA, Reck M, Rolfo C, Tan D, Vansteenkiste J, West H, Besse B. Position of an international panel of lung cancer experts on the decision for expansion of approval for pembrolizumab in advanced non-small-cell lung cancer with a PD-L1 expression level of ≥1% by the USA Food and Drug Administration. Ann Oncol 2019; 30:1686-1688. [PMID: 31504132 DOI: 10.1093/annonc/mdz295] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- G Mountzios
- Department of Medical Oncology, Henry Dunant Hospital Center, Athens, Greece.
| | - J Remon
- Department of Medical Oncology, CIOCC HM Delfos Hospital, Barcelona, Spain
| | - S Novello
- Department of Oncology, University of Turin, AOU San Luigi, Orbassano, Italy
| | - N Blais
- Centre Hospitalier Universitaire de Montréal, University of Montreal, Montreal, Canada
| | - R Califano
- Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester; Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - T Cufer
- University Clinic Golnik, Medical Faculty Ljubljana, Slovenia
| | - A M Dingemans
- Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht and Erasmus Medical Center, Rotterdam, The Netherlands
| | - S V Liu
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington, USA
| | - N Peled
- Soroka Medical Center and Ben-Gurion University, Beer-Sheva, Israel
| | - N A Pennell
- Hematology and Medical Oncology, Cleveland Clinic Taussig Cancer Institute, Cleveland, USA
| | - M Reck
- Lung Clinic Grosshansdorf, Airway Research Center North, German Center for Lung Research, Grosshansdorf, Germany
| | - C Rolfo
- Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, USA
| | - D Tan
- Division of Medical Oncology, National Cancer Centre, Singapore
| | - J Vansteenkiste
- Respiratory Oncology Unit, University Hospital KU Leuven, Leuven, Belgium
| | - H West
- Department of Medical Oncology, City of Hope Comprehensive Cancer Center, Duarte, USA
| | - B Besse
- Cancer Medicine Department, Institut Gustave Roussy, Villejuif; Université Paris-Saclay, Orsay, France
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