Prediction of biomarker-disease associations based on graph attention network and text representation.

MOTIVATION: The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS: Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY: The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.

A novel three-plasmid packaging system for chimeric SFV/SIN VRPs derived from Semliki Forest virus and Sindbis virus as a candidate gene delivery vector.

Semliki Forest virus (SFV) viral replicon particles (VRPs) have been frequently used in various animal models and clinical trials. Chimeric replicon particles offer different advantages because of their unique biological properties. We here constructed a novel three-plasmid packaging system for chimeric SFV/SIN VRPs. The capsid and envelope of SIN structural proteins were generated using two-helper plasmids separately, and the SFV replicon contained the SFV replicase gene, packaging signal of SIN, subgenomic promoter followed by the exogenous gene, and 3' UTR of SIN. The chimeric VRPs carried luciferase or eGFP as reporter genes. The fluorescence and electron microscopy results revealed that chimeric VRPs were successfully packaged. The yield of the purified chimeric VRPs was approximately 2.5 times that of the SFV VRPs (1.38 × 107 TU/ml vs. 5.41 × 106 TU/ml) (p < 0.01). Furthermore, chimeric VRPs could be stored stably at 4°C for at least 60 days. Animal experiments revealed that mice immunized with chimeric VRPs (luciferase) had stronger luciferase expression than those immunized with equivalent amount of SFV VRPs (luciferase) (p < 0.01), and successfully expressed luciferase for approximately 12 days. Additionally, the chimeric VRPs expressed the RBD of SARS-CoV-2 efficiently and induced robust RBD-specific antibody responses in mice. In conclusion, the chimeric VRPs constructed here met the requirements of a gene delivery tool for vaccine development and cancer therapy.

Substituent effects on the selectivity of ambimodal [6+4]/[4+2] cycloaddition.

In this work, we report a density functional theory (DFT) study and a dynamical trajectory study of substituent effects on the ambimodal [6+4]/[4+2] cycloaddition proposed for 1,3,5,10,12-cycloheptadecapentaene, referred to as cycloheptadecapentaene. The proposed cycloaddition proceeds through an ambimodal transition state, which results in both a [6+4] adduct a [4+2] adduct directly. The [6+4] adduct can be readily converted to the [4+2] adduct via a Cope rearrangement. We study the selectivity of the reaction with regard to the position of substituents, steric effects of substituents, and electronic effects of substituents. In the dynamical trajectory study, we find that nitro-substituted reactants lead to a new product from the ambimodal transition state via the hetero Diels-Alder reaction, and this new product can then be converted to a [4+2] adduct by a hetero [3, 3]-sigmatropic rearrangement. These results may provide insights for designing more bridged heterocyclic compounds.

Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study.

BACKGROUND: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. METHODS: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. RESULTS: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR,2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. CONCLUSION: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

Influence of Crystal Water on Crystal Structure, Electronic Structure, Band Structure, and Charge Separation of WO3·2H2O Nanosheets.

In this work, we mainly investigate the influence of structure water on crystal structure, electronic structure, band structure, and charge separation of WO3·2H2O. It is found, for the first time, that although water is a weak electron donor, a ligand-to-metal charge transfer (LMCT) from H2O to W occurs. The structure water contributes to the conduction band (CB) of WO3·2H2O, and the band gap of WO3·2H2O is obviously narrowed, thus increasing the light absorption obviously. Moreover, the results of EIS, photocurrent spectra, and PL show that structure water in WO3·2H2O also improves the charge separation and transfer efficiency of the catalyst. This is the first investigation on the LMCT transfer from structure water (a weak electron donor) to tungsten, which obviously improves light absorption and charge separation. Under visible light irradiation (λ ≥ 420 nm), WO3·2H2O nanosheets have a photocatalytic activity 2.3 times higher than that of WO3 for the degradation of methylene blue (MB). The kind number of photochemical materials can be increased greatly, because structure water-contained compounds widely exist in nature.

Enhanced energy density of a supercapacitor using 2D CoMoO4 ultrathin nanosheets and asymmetric configuration.

Developing a high energy density micro-supercapacitor still remains a big challenge. In this paper, a two-dimensional (2D) CoMoO4 ultrathin nanosheet (NS)-based asymmetric supercapacitor (ASC) is fabricated. It is found that the CoMoO4 NS electrode processes a high specific capacitance (153.2 F g-1) at a current density of 1 mA cm-2 and this ASC can deliver an energy density of 0.313 mWh cm-3 at a power density of 80 mW cm-3, which is higher than that reported in the literature. Moreover, the ASC can drive a light emitting diode (3 mm diameter, red) to work for 6 min after being charged for 10 s. After 5000 cycles, 77.37% of capacitance still remains. We maintain that the ultrathin thickness can significantly shorten the diffusion paths for both electrons and ions, thus leading to fast electron transport and ion diffusion rates. Our results demonstrate that 2D ultrathin NSs could be a new, promising candidate for energy conversion/storage devices, which could offer more accommodating sites for ion intercalation.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used. METHODS: In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance. RESULTS: A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900. CONCLUSIONS: Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

State conversion based on terahertz plasmonics with vanadium dioxide coating controlled by optical pumping.

The state conversion and terahertz (THz) wave modulation based on a plasmonic device composed of silicon column arrays with vanadium dioxide (VO2) coating were experimentally demonstrated. For double 45° tilted optical pumping, a state conversion from dielectric photonic crystal (PC) to metallic PC was demonstrated due to the insulator-metal transition (IMT) of VO2 with the pump power increasing. In this process, a broadband intensity modulation with 70% modulation depth was achieved. Furthermore, for normally incident optical pumping, another state conversion from dielectric PC to plasmonic device was also demonstrated due to the partial IMT of VO2, and the out of plane PC resonance gradually changed to be plasmonic resonances. This device and its modulation scheme will be of great significance for potential THz applications.

Nail it! vision-based drift correction for accurate mixed reality surgical guidance.

PURPOSE: Mixed reality-guided surgery through head-mounted displays (HMDs) is gaining interest among surgeons. However, precise tracking of HMDs relative to the surgical environment is crucial for successful outcomes. Without fiducial markers, spatial tracking of the HMD suffers from millimeter- to centimeter-scale drift, resulting in misaligned visualization of registered overlays. Methods and workflows capable of automatically correcting for drift after patient registration are essential to assuring accurate execution of surgical plans. METHODS: We present a mixed reality surgical navigation workflow that continuously corrects for drift after patient registration using only image-based methods. We demonstrate its feasibility and capabilities using the Microsoft HoloLens on glenoid pin placement in total shoulder arthroplasty. A phantom study was conducted involving five users with each user placing pins on six glenoids of different deformity, followed by a cadaver study by an attending surgeon. RESULTS: In both studies, all users were satisfied with the registration overlay before drilling the pin. Postoperative CT scans showed 1.5 mm error in entry point deviation and 2.4[Formula: see text] error in pin orientation on average in the phantom study and 2.5 mm and 1.5[Formula: see text] in the cadaver study. A trained user takes around 90 s to complete the workflow. Our method also outperformed HoloLens native tracking in drift correction. CONCLUSION: Our findings suggest that image-based drift correction can provide mixed reality environments precisely aligned with patient anatomy, enabling pin placement with consistently high accuracy. These techniques constitute a next step toward purely image-based mixed reality surgical guidance, without requiring patient markers or external tracking hardware.

Design of biodegradable polyurethanes and post-modification with long alkyl chains via inhibiting biofilm formation and killing drug-resistant bacteria for the treatment of wound bacterial infection.

The development of cationic polymers that simulate antimicrobial peptides to treat bacterial infections has received much research interest. In order to obtain polymers that can not only eradicate bacteria but also inhibit biofilm formation, without inducing bacterial drug resistance, a series of cationic polymers have been developed. Despite recent progress, the chemical structures of these polymers are stable, making them recalcitrant to biodegradation and metabolism within organisms, potentially inducing long-term toxicity. To overcome this limitation, herein, a novel strategy of designing biodegradable polyurethanes with tertiary amines and quaternary ammonium salts via condensation polymerization and post-functionalizing them is reported. These polymers were found to exhibit potent antibacterial activity against Staphylococcus aureus and Escherichia coli, effectively prevent the formation of Staphylococcus aureus biofilms, act quickly and effectively against bacteria and display no resistance after repeated use. In addition, the potent in vivo antibacterial effects of these antimicrobial polyurethanes in a mouse model with methicillin-resistant Staphylococcus aureus skin infection are demonstrated.

Increased brain iron in patients with thyroid-associated ophthalmopathy: a whole-brain analysis.

Background: To investigate the whole-brain iron deposition alternations in patients with thyroid-associated ophthalmopathy (TAO) using quantitative susceptibility mapping (QSM). Methods: Forty-eight patients with TAO and 33 healthy controls (HCs) were enrolled. All participants underwent brain magnetic resonance imaging scans and clinical scale assessments. QSM values were calculated and compared between TAO and HCs groups using a voxel-based analysis. A support vector machine (SVM) analysis was performed to evaluate the performance of QSM values in differentiating patients with TAO from HCs. Results: Compared with HCs, patients with TAO showed significantly increased QSM values in the bilateral caudate nucleus (CN), left thalamus (TH), left cuneus, left precuneus, right insula and right middle frontal gyrus. In TAO group, QSM values in left TH were positively correlated with Hamilton Depression Rating Scale (HDRS) scores (r = 0.414, p = 0.005). The QSM values in right CN were negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r = -0.342, p = 0.021). Besides that, a nearly negative correlation was found between QSM values in left CN and MoCA scores (r = -0.286, p = 0.057). The SVM model showed a good performance in distinguishing patients with TAO from the HCs (area under the curve, 0.958; average accuracy, 90.1%). Conclusion: Patients with TAO had significantly increased iron deposition in brain regions corresponding to known visual, emotional and cognitive deficits. QSM values could serve as potential neuroimaging markers of TAO.

Mixed Reality Interfaces for Achieving Desired Views with Robotic X-ray Systems.

Robotic X-ray C-arm imaging systems can precisely achieve any position and orientation relative to the patient. Informing the system, however, what pose exactly corresponds to a desired view is challenging. Currently these systems are operated by the surgeon using joysticks, but this interaction paradigm is not necessarily effective because users may be unable to efficiently actuate more than a single axis of the system simultaneously. Moreover, novel robotic imaging systems, such as the Brainlab Loop-X, allow for independent source and detector movements, adding even more complexity. To address this challenge, we consider complementary interfaces for the surgeon to command robotic X-ray systems effectively. Specifically, we consider three interaction paradigms: (1) the use of a pointer to specify the principal ray of the desired view relative to the anatomy, (2) the same pointer, but combined with a mixed reality environment to synchronously render digitally reconstructed radiographs from the tool's pose, and (3) the same mixed reality environment but with a virtual X-ray source instead of the pointer. Initial human-in-the-loop evaluation with an attending trauma surgeon indicates that mixed reality interfaces for robotic X-ray system control are promising and may contribute to substantially reducing the number of X-ray images acquired solely during "fluoro hunting" for the desired view or standard plane.

Application of Prognostic Models Based on Psoas Muscle Index, Stage, Pathological Grade, and Preoperative Carcinoembryonic Antigen Level in Stage II-III Colorectal Cancer Patients Undergoing Adjuvant Chemotherapy.

OBJECTIVE: To investigate the effect of sarcopenia on the prognosis of stage II-III colorectal cancer patients undergoing adjuvant chemotherapy. METHODS: A total of 196 stage II-III colorectal cancer patients who received 8 cycles of postoperative chemotherapy were retrospectively analyzed. An abdominal CT acquired at 3-4 weeks after surgery was used to calculate the psoas muscle index. Subsequently, once gender-specific receiver operating characteristic curves were plotted and cut-off values of psoas muscle index were defined, the clinicopathological characteristics and the prognosis of patients with high and low values were compared. Lastly, prognostic models were established based on the independent prognostic factors of relapse-free survival and overall survival identified by COX analysis. RESULTS: Based on the psoas muscle index, the prevalence of sarcopenia was 37.5% among 196 patients. This prevalence has significant correlation with patients' age and gender. However, it was not related to the AJCC stage, T stage, lymph node metastasis, pathological grade, grade III-IV myelosuppression, or preoperative carcinoembryonic antigen level. In addition, both the relapse-free and the overall survival of patients with low and high psoas muscle indexes were significantly different. COX analysis indicated that the psoas muscle index was an independent prognostic factor. Both the overall survival prognostic model based on patients' psoas muscle index, stage, pathological grade, and preoperative carcinoembryonic antigen level and the relapse-free survival prognostic model based on patients' psoas muscle index, pathological grade, and preoperative carcinoembryonic antigen level could accurately predict the prognosis of patients. CONCLUSION: For stage II-III colorectal cancer patients, the presence of sarcopenia before adjuvant chemotherapy would adversely affect their recurrence-free and overall survival. Prognostic models based on psoas muscle index, stage, pathological grade, and preoperative carcinoembryonic antigen level could accurately predict the prognosis in these patients.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.

Motivation: Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development. Results: To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies. Availability and implementation: MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

The impact of visualization paradigms on the detectability of spatial misalignment in mixed reality surgical guidance.

PURPOSE: Mixed reality (MR) for image-guided surgery may enable unobtrusive solutions for precision surgery. To display preoperative treatment plans at the correct physical position, it is essential to spatially align it with the patient intra-operatively. Accurate alignment is safety critical because it will guide treatment, but cannot always be achieved for varied reasons. Effective visualization mechanisms that reveal misalignment are crucial to prevent adverse surgical outcomes to ensure safe execution. METHODS: We test the effectiveness of three MR visualization paradigms in revealing spatial misalignment: wireframe, silhouette, and heatmap, which encodes residual registration error. We conduct a user study among 12 participants and use an anthropomorphic phantom mimicking total shoulder arthroplasty. Participants wearing Microsoft HoloLens 2 are presented with 36 randomly ordered spatial (mis)alignments of a virtual glenoid model overlaid on the phantom, each rendered using one of the three methods. Users choose whether to accept or reject the spatial alignment at every trial. Upon completion, participants report their perceived difficulty while using the visualization paradigms. RESULTS: Across all visualization paradigms, the ability of participants to reliably judge the accuracy of spatial alignment was moderate (58.33%).The three visualization paradigms showed comparable performance. However, the heatmap-based visualization resulted in significantly better detectability than random chance ([Formula: see text]). Despite heatmap enabling the most accurate decisions according to our measurements, wireframe was the most liked paradigm (50 %), followed by silhouette (41.7 %) and heatmap (8.3 %). CONCLUSION: Our findings suggest that conventional mixed reality visualization paradigms are not sufficiently effective in enabling users to differentiate between accurate and inaccurate spatial alignment of virtual content to the environment.

Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.

Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful updates of X-ray pose and reduce the likelihood of getting trapped in a local minimum. The conventional approaches strongly depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) registration solution that captures large-range pose relations by extracting both local and contextual information, yielding meaningful X-ray pose updates without the need for accurate initialization. To register a 2D X-ray image and a 3D CT scan, our CNN accepts a target X-ray image and a digitally reconstructed radiograph at the current pose estimate as input and iteratively outputs pose updates in the direction of the pose gradient on the Riemannian Manifold. Our approach integrates seamlessly with conventional image-based registration frameworks, where long-range relations are captured primarily by our CNN-based method while short-range offsets are recovered accurately with an image similarity-based method. On both synthetic and real X-ray images of the human pelvis, we demonstrate that the proposed method can successfully recover large rotational and translational offsets, irrespective of initialization.

Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration.

Differentiable rendering is a technique to connect 3D scenes with corresponding 2D images. Since it is differentiable, processes during image formation can be learned. Previous approaches to differentiable rendering focus on mesh-based representations of 3D scenes, which is inappropriate for medical applications where volumetric, voxelized models are used to represent anatomy. We propose a novel Projective Spatial Transformer module that generalizes spatial transformers to projective geometry, thus enabling differentiable volume rendering. We demonstrate the usefulness of this architecture on the example of 2D/3D registration between radiographs and CT scans. Specifically, we show that our transformer enables end-to-end learning of an image processing and projection model that approximates an image similarity function that is convex with respect to the pose parameters, and can thus be optimized effectively using conventional gradient descent. To the best of our knowledge, we are the first to describe the spatial transformers in the context of projective transmission imaging, including rendering and pose estimation. We hope that our developments will benefit related 3D research applications. The source code is available at https://github.com/gaocong13/Projective-Spatial-Transformers.

Interactive Flying Frustums (IFFs): spatially aware surgical data visualization.

PURPOSE: As the trend toward minimally invasive and percutaneous interventions continues, the importance of appropriate surgical data visualization becomes more evident. Ineffective interventional data display techniques that yield poor ergonomics that hinder hand-eye coordination, and therefore promote frustration which can compromise on-task performance up to adverse outcome. A very common example of ineffective visualization is monitors attached to the base of mobile C-arm X-ray systems. METHODS: We present a spatially and imaging geometry-aware paradigm for visualization of fluoroscopic images using Interactive Flying Frustums (IFFs) in a mixed reality environment. We exploit the fact that the C-arm imaging geometry can be modeled as a pinhole camera giving rise to an 11-degree-of-freedom view frustum on which the X-ray image can be translated while remaining valid. Visualizing IFFs to the surgeon in an augmented reality environment intuitively unites the virtual 2D X-ray image plane and the real 3D patient anatomy. To achieve this visualization, the surgeon and C-arm are tracked relative to the same coordinate frame using image-based localization and mapping, with the augmented reality environment being delivered to the surgeon via a state-of-the-art optical see-through head-mounted display. RESULTS: The root-mean-squared error of C-arm source tracking after hand-eye calibration was determined as [Formula: see text] and [Formula: see text] in rotation and translation, respectively. Finally, we demonstrated the application of spatially aware data visualization for internal fixation of pelvic fractures and percutaneous vertebroplasty. CONCLUSION: Our spatially aware approach to transmission image visualization effectively unites patient anatomy with X-ray images by enabling spatial image manipulation that abides image formation. Our proof-of-principle findings indicate potential applications for surgical tasks that mostly rely on orientational information such as placing the acetabular component in total hip arthroplasty, making us confident that the proposed augmented reality concept can pave the way for improving surgical performance and visuo-motor coordination in fluoroscopy-guided surgery.

The feasibility of 1-stop examination of coronary CT angiography and abdominal enhanced CT.

This study aims to evaluate the feasibility of performing coronary computed tomography angiography (CCTA) and abdominal enhanced computed tomography (CT) with 1-time injection of the agent.CCTA images (right coronary artery, left anterior descending coronary artery, and left circumflex coronary artery) were collected from 20 patients who completed a 1-stop combined examination of CCTA and abdominal enhanced CT (group A), 20 patients who only underwent abdominal enhanced CT (group B1), and 20 patients who only underwent CCTA (group B2). These images were interpreted using the 5-point Likert scale system by 2 experienced radiologists, and abdominal images were observed for breathing artifact. CT value, signal-to-noise ratio (SNR), and CTDI were recorded and compare among the 3 groups.The difference in image quality of the coronary and total volume of the contrast agent between group A and group B1 was not statistical significant (P > .05). The CT value and SNR in group B1 (CCTA) (CT: 394.65 ±â€Š59.23, SNR: 17.38 ±â€Š4.13) increased, compare with Group A (CT: 360.35 ±â€Š34.16, SNR: 13.76 ±â€Š1.84, P = .03, .01), while CTDI was undifferentiated between group A (17.14 ±â€Š6.20) and group B1 (18.38 ±â€Š9.79) (P = .64). The difference in CT value and SNR at the arterial phase and CT value at the venous phase between group A (abdomen) and group B2 were statistically significant, the CTDI in group A (9.09 ±â€Š1.05) increased, compared with group B2 (8.23 ±â€Š1.33) (P = .03), and SNR at the venous phase in group B2 (12.50 ±â€Š2.43) increased, compared with group A (10.89 ±â€Š2.03) (P = .03).Revolution CT can capture full images and very rapidly switch to the scan mode, enabling a 1-stop axial CCTA and enhanced helical abdominal scan. The 1-stop combined scan resulted in a satisfactory image quality, which reduced the contrast agent dose and simplified the workflow.The 1-stop combined scan allows for the high success rate of the examination, reduces the number of examinations, and decreases the dose and risk of injection of the contrast agent. This would be helpful for patients to obtain diagnostic images in time.