The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

Demonstrating Path-Independent Anyonic Braiding on a Modular Superconducting Quantum Processor.

Anyons, exotic quasiparticles in two-dimensional space exhibiting nontrivial exchange statistics, play a crucial role in universal topological quantum computing. One notable proposal to manifest the fractional statistics of anyons is the toric code model; however, scaling up its size through quantum simulation poses a serious challenge because of its highly entangled ground state. In this Letter, we demonstrate that a modular superconducting quantum processor enables hardware-pragmatic implementation of the toric code model. Through in-parallel control across separate modules, we generate a 10-qubit toric code ground state in four steps and realize six distinct braiding paths to benchmark the performance of anyonic statistics. The path independence of the anyonic braiding statistics is verified by correlation measurements in an efficient and scalable fashion. Our modular approach, serving as a hardware embodiment of the toric code model, offers a promising avenue toward scalable simulation of topological phases, paving the way for quantum simulation in a distributed fashion.

Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes.

Genome alteration signatures reflect recurring patterns caused by distinct endogenous or exogenous mutational events during the evolution of cancer. Signatures of single base substitution (SBS) have been extensively studied in different types of cancer. Copy number alterations are important drivers for the progression of multiple cancer. However, practical tools for studying the signatures of copy number alterations are still lacking. Here, a user-friendly open source bioinformatics tool "sigminer" has been constructed for copy number signature extraction, analysis and visualization. This tool has been applied in prostate cancer (PC), which is particularly driven by complex genome alterations. Five copy number signatures are identified from human PC genome with this tool. The underlying mutational processes for each copy number signature have been illustrated. Sample clustering based on copy number signature exposure reveals considerable heterogeneity of PC, and copy number signatures show improved PC clinical outcome association when compared with SBS signatures. This copy number signature analysis in PC provides distinct insight into the etiology of PC, and potential biomarkers for PC stratification and prognosis.

UCSCXenaShiny: an R/CRAN package for interactive analysis of UCSC Xena data.

SUMMARY: UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer. AVAILABILITY AND IMPLEMENTATION: UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experimental Realization of Two Qutrits Gate with Tunable Coupling in Superconducting Circuits.

Gate-based quantum computation has been extensively investigated using quantum circuits based on qubits. In many cases, such qubits are actually made out of multilevel systems but with only two states being used for computational purpose. While such a strategy has the advantage of being in line with the common binary logic, it in some sense wastes the ready-for-use resources in the large Hilbert space of these intrinsic multidimensional systems. Quantum computation beyond qubits (e.g., using qutrits or qudits) has thus been discussed and argued to be more efficient than its qubit counterpart in certain scenarios. However, one of the essential elements for qutrit-based quantum computation, two-qutrit quantum gate, remains a major challenge. In this Letter, we propose and demonstrate a highly efficient and scalable two-qutrit quantum gate in superconducting quantum circuits. Using a tunable coupler to control the cross-Kerr coupling between two qutrits, our scheme realizes a two-qutrit conditional phase gate with fidelity 89.3% by combining simple pulses applied to the coupler with single-qutrit operations. We further use such a two-qutrit gate to prepare an EPR state of two qutrits with a fidelity of 95.5%. Our scheme takes advantage of a tunable qutrit-qutrit coupling with a large on:off ratio. It therefore offers both high efficiency and low crosstalk between qutrits, thus being friendly for scaling up. Our Letter constitutes an important step toward scalable qutrit-based quantum computation.

Sigflow: an automated and comprehensive pipeline for cancer genome mutational signature analysis.

SUMMARY: Mutational signatures are recurring DNA alteration patterns caused by distinct mutational events during the evolution of cancer. In recent years, several bioinformatics tools are available for mutational signature analysis. However, most of them focus on specific type of mutation or have limited scope of application. A pipeline tool for comprehensive mutational signature analysis is still lacking. Here we present Sigflow pipeline, which provides an one-stop solution for de novo signature extraction, reference signature fitting, signature stability analysis, sample clustering based on signature exposure in different types of genome DNA alterations including single base substitution, doublet base substitution, small insertion and deletion and copy number alteration. A Docker image is constructed to solve the complex and time-consuming installation issues, and this enables reproducible research by version control of all dependent tools along with their environments. Sigflow pipeline can be applied to both human and mouse genomes. AVAILABILITY AND IMPLEMENTATION: Sigflow is an open source software under academic free license v3.0 and it is freely available at https://github.com/ShixiangWang/sigflow or https://hub.docker.com/r/shixiangwang/sigflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma.

BACKGROUND: The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features. METHODS: Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model. RESULTS: Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion. CONCLUSIONS: The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.

Identification of line-source transfer mobility and force density by ground acceleration measurements.

Ground-borne vibration caused by railway traffic has been a research concern due to its possible side effects on nearby residences. The force density and line-source mobility can effectively characterize the generation and transmission of train-induced vibrations, respectively. This research proposed a frequency-domain method for identifying the line-source transfer mobility and force density using measured vibrations at the ground surface, which was on the basis of the least-square method. The proposed method was applied to a case study at Shenzhen Metro in China, where a total of seven fixed-point hammer impacts with 3.3 m equal intervals were used to represent the train vibration excitations. Line-source transfer mobility of the site and force density levels of the metro train were identified, respectively. Causes for different dominant frequencies can be traced by separating the dynamic characteristics of vibration excitation and transmission. It was found in the case study that at a location 3 m away from the track, the peak at 50 Hz was caused by excitations, while that at 63 Hz was attributed to transmission efficiency related to the soil properties. Subsequently, numerical validations of the fixed-point loads' assumption and identified force density levels were carried out. Good comparisons between numerically predicted and experimentally identified force density levels indicated the feasibility of the proposed method. At last, the identified line-source transfer mobility and force density levels were applied to the forward problem, i.e., making predictions of train-induced vibrations. The predicted ground and structural vibrations at different locations were compared to corresponding measurements, with good agreement, which experimentally validated the identification method. The identification results of the case study can be employed by similar railway systems as a good reference.

Genetic analysis of potential biomarkers and therapeutic targets associated with ferroptosis from bronchopulmonary dysplasia.

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively. A total of 171 BPD-related differentially expressed ferroptosis-related genes (DE-FRGs) linked to the regulation of autophagy and immune response were identified. Least absolute shrinkage and selection operator and SVM-RFE algorithms identified 23 and 14 genes, respectively, as marker genes. The intersection of these 2 sets yielded 9 genes (ALOX12B, NR1D1, LGMN, IFNA21, MEG3, AKR1C1, CA9, ABCC5, and GALNT14) with acceptable diagnostic capacity. The results of the functional enrichment analysis indicated that these identified marker genes may be involved in the pathogenesis of BPD through the regulation of immune response, cell cycle, and BPD-related pathways. Additionally, we identified 29 drugs that target 5 of the marker genes, which could have potential therapeutic implications. The ceRNA network we constructed revealed a complex regulatory network based on the marker genes, further highlighting their potential roles in BPD. Our findings offer diagnostic potential and insight into the mechanism underlying BPD. Further research is needed to assess its clinical utility.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

Copy number alteration features in pan-cancer homologous recombination deficiency prediction and biology.

Homologous recombination deficiency (HRD) renders cancer cells vulnerable to unrepaired double-strand breaks and is an important therapeutic target as exemplified by the clinical efficacy of poly ADP-ribose polymerase (PARP) inhibitors as well as the platinum chemotherapy drugs applied to HRD patients. However, it remains a challenge to predict HRD status precisely and economically. Copy number alteration (CNA), as a pervasive trait of human cancers, can be extracted from a variety of data sources, including whole genome sequencing (WGS), SNP array, and panel sequencing, and thus can be easily applied clinically. Here we systematically evaluate the predictive performance of various CNA features and signatures in HRD prediction and build a gradient boosting machine model (HRDCNA) for pan-cancer HRD prediction based on these CNA features. CNA features BP10MB[1] (The number of breakpoints per 10MB of DNA is 1) and SS[ > 7 & <=8] (The log10-based size of segments is greater than 7 and less than or equal to 8) are identified as the most important features in HRD prediction. HRDCNA suggests the biallelic inactivation of BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BARD1 as the major genetic basis for human HRD, and may also be applied to effectively validate the pathogenicity of BRCA1/2 variants of uncertain significance (VUS). Together, this study provides a robust tool for cost-effective HRD prediction and also demonstrates the applicability of CNA features and signatures in cancer precision medicine.

Quantification of Neoantigen-Mediated Immunoediting in Cancer Evolution.

Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual patients with cancer. Moreover, this method demonstrated the prevalence of immunoediting in the immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer. SIGNIFICANCE: Quantification of neoantigen-mediated negative selection in cancer progression reveals distinct features of cancer immunoediting and can serve as a potential biomarker to predict immunotherapy response.

Extrachromosomal DNA formation enables tumor immune escape potentially through regulating antigen presentation gene expression.

Extrachromosomal DNA (ecDNA) is a type of circular and tumor specific genetic element. EcDNA has been reported to display open chromatin structure, facilitate oncogene amplification and genetic material unequal segregation, and is associated with poor cancer patients' prognosis. The ability of immune evasion is a typical feature for cancer progression, however the tumor intrinsic factors that determine immune evasion remain poorly understood. Here we show that the presence of ecDNA is associated with markers of tumor immune evasion, and obtaining ecDNA could be one of the mechanisms employed by tumor cells to escape immune surveillance. Tumors with ecDNA usually have comparable TMB and neoantigen load, however they have lower immune cell infiltration and lower cytotoxic T cell activity. The microenvironment of tumors with ecDNA shows increased immune-depleted, decreased immune-enriched fibrotic types. Both MHC class I and class II antigen presentation genes' expression are decreased in tumors with ecDNA, and this could be the underlying mechanism for ecDNA associated immune evasion. This study provides evidence that ecDNA formation is an immune escape mechanism for cancer cells.

Identification and immunological characterization of endoplasmic reticulum stress-related molecular subtypes in bronchopulmonary dysplasia based on machine learning.

Introduction: Bronchopulmonary dysplasia (BPD) is a life-threatening lung illness that affects premature infants and has a high incidence and mortality. Using interpretable machine learning, we aimed to investigate the involvement of endoplasmic reticulum (ER) stress-related genes (ERSGs) in BPD patients. Methods: We evaluated the expression profiles of endoplasmic reticulum stress-related genes and immune features in bronchopulmonary dysplasia using the GSE32472 dataset. The endoplasmic reticulum stress-related gene-based molecular clusters and associated immune cell infiltration were studied using 62 bronchopulmonary dysplasia samples. Cluster-specific differentially expressed genes (DEGs) were identified utilizing the WGCNA technique. The optimum machine model was applied after comparing its performance with that of the generalized linear model, the extreme Gradient Boosting, the support vector machine (SVM) model, and the random forest model. Validation of the prediction efficiency was done by the use of a calibration curve, nomogram, decision curve analysis, and an external data set. Results: The bronchopulmonary dysplasia samples were compared to the control samples, and the dysregulated endoplasmic reticulum stress-related genes and activated immunological responses were analyzed. In bronchopulmonary dysplasia, two distinct molecular clusters associated with endoplasmic reticulum stress were identified. The analysis of immune cell infiltration indicated a considerable difference in levels of immunity between the various clusters. As measured by residual and root mean square error, as well as the area under the curve, the support vector machine machine model showed the greatest discriminative capacity. In the end, an support vector machine model integrating five genes was developed, and its performance was shown to be excellent on an external validation dataset. The effectiveness in predicting bronchopulmonary dysplasia subtypes was further established by decision curves, calibration curves, and nomogram analyses. Conclusion: We developed a potential prediction model to assess the risk of endoplasmic reticulum stress subtypes and the clinical outcomes of bronchopulmonary dysplasia patients, and our work comprehensively revealed the complex association between endoplasmic reticulum stress and bronchopulmonary dysplasia.

Pan-cancer noncoding genomic analysis identifies functional CDC20 promoter mutation hotspots.

Noncoding DNA sequences occupy more than 98% of the human genome; however, few cancer noncoding drivers have been identified compared with cancer coding drivers, probably because cancer noncoding drivers have a distinct mutation pattern due to the distinct function of noncoding DNA. Here we performed pan-cancer whole genome mutation analysis to screen for functional noncoding mutations that influence protein factor binding. Recurrent mutations were identified in the promoter of CDC20 gene. These CDC20 promoter hotspot mutations disrupt the binding of ELK4 transcription repressor, lead to the up-regulation of CDC20 transcription. Physiologically ELK4 binds to the unmutated hotspot sites and is involved in DNA damage-induced CDC20 transcriptional repression. Overall, our study not only identifies a detailed mechanism for CDC20 gene deregulation in human cancers but also finds functional noncoding genetic alterations, with implications for the further development of function-based noncoding driver discovery pipelines.

Simulation of higher-order topological phases and related topological phase transitions in a superconducting qubit.

Higher-order topological phases give rise to new bulk and boundary physics, as well as new classes of topological phase transitions. While the realization of higher-order topological phases has been confirmed in many platforms by detecting the existence of gapless boundary modes, a direct determination of the higher-order topology and related topological phase transitions through the bulk in experiments has still been lacking. To bridge the gap, in this work we carry out the simulation of a two-dimensional second-order topological phase in a superconducting qubit. Owing to the great flexibility and controllability of the quantum simulator, we observe the realization of higher-order topology directly through the measurement of the pseudo-spin texture in momentum space of the bulk for the first time, in sharp contrast to previous experiments based on the detection of gapless boundary modes in real space. Also through the measurement of the evolution of pseudo-spin texture with parameters, we further observe novel topological phase transitions from the second-order topological phase to the trivial phase, as well as to the first-order topological phase with nonzero Chern number. Our work sheds new light on the study of higher-order topological phases and topological phase transitions.

Assessment of ventilation noise impact from metro depot with over-track platform structure on workers and nearby inhabitants.

In order to alleviate the shortage of land use accompanied with urbanization, over-track building complexes above metro depots are developed rapidly in China, resulting in the larger mechanical ventilation needs in the metro depot. Excessive noise exposure caused by the huge ventilation system will bring serious impacts on the health of workers in the metro depot. This research intends to evaluate the noise annoyance in the metro depot and the noise influences on adjacent residential buildings by the ventilation system. A questionnaire survey of 100 people working in a metro depot of Guangzhou Metro Line 6 was conducted, and field measurements inside the metro depot and inside adjacent residential buildings were carried out. The environmental noise level in the metro depot and the ventilation fan-induced noise level inside adjacent residential buildings exceed the corresponding criterion limit of China. Dose-response relationship modeled by logistic function was used to assess the noise annoyance, which is found underestimating the percentage of "Annoyed" (% A) and percentage of "Little Annoyed" (% LA) of staffs in the metro depot, especially for the % LA. This may be attributed to the reducing reliability of dose-effect curves as the Ldn is higher than 65 dB(A). In order to alleviate the noise impact, noise-controlling measures need to be took. However, traditional noise-controlling measures cannot solve low-frequency noise disturbance.

The impacts of transcatheter occlusion for congenital atrial septal defect on atrial volume, function, and synchronicity in children: a three-dimensional echocardiography study.

OBJECTIVE: To evaluate the impacts of transcatheter closure for atrial septal defect (ASD) on the atria. METHODS: Thirty-four patients with ASD undergoing transcatheter occlusion were recruited in the study, and 34 patients undergoing surgical operation and 34 healthy children were age-matched as controls. A real time three-dimensional (RT3DE) echocardiography was used to measure the volume, function, and synchronicity of the atria. RESULTS: There was no difference in the atrial volume and function between the transcatheter occlusion group and healthy control group (P > 0.05). However, the parameters reflecting the atrial asynchrony were larger in the transcatheter occlusion group (P < 0.05). Compared to the surgical repair group, the transcatheter occlusion group had smaller maximum volume of the left atrium (21.0 +/- 5.2 ml/m(2) vs 24.3 +/- 5.8 ml/m(2), P = 0.01), smaller total emptying volume of the left atrium (12.7 +/- 4.3 ml/m(2) vs 15.1 +/- 3.8 ml/m(2), P = 0.014), smaller total emptying volume of the right atrium (13.5 +/- 5.2 ml/m(2) vs 16.1 +/- 4.7 ml/m(2), P = 0.029), and larger atrial systolic asynchrony indices. CONCLUSIONS: An atrial asynchrony is observed in patients with transcatheter closure of ASD, although little negative impacts on the atrial volume and function are demonstrated, which deserves more attention during follow-up of this specific population.

[The study of vascular lesion using ultrasound in children with a history of Kawasaki disease].

OBJECTIVE: To explore the endothelial function, carotid artery stiffness index and carotid intima-media thickness in children with a history of Kawasaki disease (KD). METHODS: A cohort of 204 children was studied, which comprised 51 patients with Kawasaki disease with coronary artery lesion (CAL group), 50 patients with Kawasaki disease with normal coronary arteries (normal coronary arteries group), and 103 healthy age matched children (control group). Their systemic blood pressure, fasting cholesterol concentrations, flow-mediated dilation (FMD) of the brachial artery, carotid artery stiffness index and carotid intima-media thickness (IMT) were compared. RESULTS: FMD of the brachial artery in CAL group (5.2% +/- 1.9%) and normal coronary arteries group (6.8% +/- 2.0%) were significantly lower than that of control group (13.2% +/- 4.1%, both P < 0. 01); carotid artery stiffness index in CAL group (4.0 +/- 0.6) was significantly greater than that of normal coronary arteries group (3.6 +/- 0.6) and control group (3.4 +/- 0.5, P = 0.05, P < 0.01); likewise, IMT in CAL group (0.447 mm +/- 0.024 mm) was significantly higher than that of normal coronary arteries group (0.426 mm +/- 0.016 mm) and control group (0.424 mm +/- 0.016 mm, both P < 0.01). In multiple linear regression analysis, age and patient grouping were significant determinants of carotid artery stiffness index, FMD and IMT. There were significant correlations between carotid artery stiffness index, FMD and IMT in both healthy group and KD group after adjustment of age. CONCLUSION: There were endothelial dysfunction and increased systemic arterial stiffness and IMT late after the acute phase of Kawasaki disease. flow-mediated dilation of the brachial artery, carotid artery stiffness index combined carotid intima-media thickness can evaluated completely the prognosis of vascular lesion in convalescence after KD.

Methodological study on real-time three-dimensional echo-cardiography and its application in the diagnosis of complex congenital heart disease.

BACKGROUND: Real-time three-dimensional echocardiography (RT-3DE) has made revolutionized improvements of cardiac imaging during the past few years. However, there is no standard examination method for RT-3DE so far. This study aimed to establish the diagnostic method of RT-3DE and evaluate its application in the diagnosis of complex congenital heart diseases (CHD). METHODS: Fifty patients with complex CHD were examined by RT-3DE with modes of Live 3DE and Full Volume. A series of novel volumetric views combined with Van Praagh sequential segmental approach were introduced to reveal the pathological morphology of the hearts, which were compared with the findings of two-dimensional echocardiography (2DE), angiography and cardiac surgery. RESULTS: In 50 patients, 190 image acquisitions of Full Volume were performed at several acoustic windows including subcostal, apical and parasternal regions. Among them, 94.2% (179/190) of image acquisitions were successful. Most sectional volumetric views could be clearly displayed in 92.6% of the successful image acquisitions. However, sectional volumetric views could not be clearly displayed in 7.4%, which was mainly due to poor perspective conditions of examination location, improper instrument multi-parameter setting and insufficient information of whole heart captured in Full Volume acquisitions. As compared with surgical findings and angiography, RT-3DE made correction to the diagnoses in 2 cases including 1 with corrected transposition of the great arteries and the other with single atrium and mitral cleft. The diagnoses initially made by 2DE for these 2 patients were double outlet right ventricle with transposition of the great arteries and complete atrio-ventricular septal defect. CONCLUSIONS: RT-3DE can clearly display the pathological morphology of complex CHD by a series of novel volumetric views combined with sequential segmental approach through providing more spatial informative cardiovascular structures, which provides a practical method for RT-3DE diagnosis.