UBE2M Is a Stress-Inducible Dual E2 for Neddylation and Ubiquitylation that Promotes Targeted Degradation of UBE2F.

UBE2M and UBE2F are two family members of neddylation E2 conjugating enzyme that, together with E3s, activate CRLs (Cullin-RING Ligases) by catalyzing cullin neddylation. However, whether and how two E2s cross-talk with each other are largely unknown. Here, we report that UBE2M is a stress-inducible gene subjected to cis-transactivation by HIF-1 and AP1, and MLN4924, a small molecule inhibitor of E1 NEDD8-activating enzyme (NAE), upregulates UBE2M via blocking degradation of HIF-1α and c-JUN. UBE2M is a dual E2 for targeted ubiquitylation and degradation of UBE2F, acting as a neddylation E2 to activate CUL3-Keap1 E3 under physiological conditions but as a ubiquitylation E2 for Parkin-DJ-1 E3 under stressed conditions. UBE2M-induced UBE2F degradation leads to CRL5 inactivation and subsequent NOXA accumulation to suppress the growth of lung cancer cells. Collectively, our study establishes a negative regulatory axis between two neddylation E2s with UBE2M ubiquitylating UBE2F, and two CRLs with CRL3 inactivating CRL5.

Bidirectional pH Buffer Effect Facilitates High-Reversible Aqueous Zinc Ion Batteries.

Aqueous zinc ion batteries (AZIBs) stand out from the crowd of energy storage equipment for their superior energy density, enhanced safety features, and affordability. However, the notorious side reaction in the zinc anode and the dissolution of the cathode materials led to poor cycling stability has hindered their further development. Herein, ammonium salicylate (AS) is a bidirectional electrolyte additive to promote prolonged stable cycles in AZIBs. NH4 + and C6H4OHCOO- collaboratively stabilize the pH at the interface of the electrolyte/electrode and guide the homogeneous deposition of Zn2+ at the zinc anode. The higher adsorption energy of NH4 + compared to H2O on the Zn (002) crystal plane mitigates the side reactions on the anode surface. Moreover, NH4 + is similarly adsorbed on the cathode surface, maintaining the stability of the electrode. C6H4OHCOO- and Zn2+ are co-intercalation/deintercalation during the cycling process, contributing to the higher electrochemical performance of the full cell. As a result, with the presence of AS additive, the Zn//Zn symmetric cells achieved 700 h of highly reversible cycling at 5 mA cm-2. In addition, the assembled NH4V4O10(NVO)//Zn coin and pouch batteries achieved higher capacity and higher cycle lifetime, demonstrating the practicality of the AS electrolyte additive.

Trace Amounts of Multifunctional Electrolyte Additives Enhance Cyclic Stability of High-Rate Aqueous Zinc-Ion Batteries.

Aqueous zinc ion batteries (AZIBs) are renowned for their exceptional safety and eco-friendliness. However, they face cycling stability and reversibility challenges, particularly under high-rate conditions due to corrosion and harmful side reactions. This work introduces fumaric acid (FA) as a trace amount, suitable high-rate, multifunctional, low-cost, and environmentally friendly electrolyte additive to address these issues. FA additives serve as prioritized anchors to form water-poor Inner Helmholtz Plane on Zn anodes and adsorb chemically on Zn anode surfaces to establish a unique in situ solid-electrolyte interface. The combined mechanisms effectively inhibit dendrite growth and suppress interfacial side reactions, resulting in excellent stability of Zn anodes. Consequently, with just tiny quantities of FA, Zn anodes achieve a high Coulombic efficiency (CE) of 99.55 % and exhibit a remarkable lifespan over 2580 hours at 5 mA cm-2, 1 mAh cm-2 in Zn//Zn cells. Even under high-rate conditions (10 mA cm-2, 1 mAh cm-2), it can still run almost for 2020 hours. Additionally, the Zn//V2O5 full cell with FA retains a high specific capacity of 106.95 mAh g-1 after 2000 cycles at 5 A g-1. This work provides a novel additive for the design of electrolytes for high-rate AZIBs.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Mechanical guidance to self-organization and pattern formation of stem cells.

The self-organization of stem cells (SCs) constitutes the fundamental basis of the development of biological organs and structures. SC-driven patterns are essential for tissue engineering, yet unguided SCs tend to form chaotic patterns, impeding progress in biomedical engineering. Here, we show that simple geometric constraints can be used as an effective mechanical modulation approach that promotes the development of controlled self-organization and pattern formation of SCs. Using the applied SC guidance with geometric constraints, we experimentally uncover a remarkable deviation in cell aggregate orientation from a random direction to a specific orientation. Subsequently, we propose a dynamic mechanical framework, including cells, the extracellular matrix (ECM), and the culture environment, to characterize the specific orientation deflection of guided cell aggregates relative to initial geometric constraints, which agrees well with experimental observation. Based on this framework, we further devise various theoretical strategies to realize complex biological patterns, such as radial and concentric structures. Our study highlights the key role of mechanical factors and geometric constraints in governing SCs' self-organization. These findings yield critical insights into the regulation of SC-driven pattern formation and hold great promise for advancements in tissue engineering and bioactive material design for regenerative application.

A novel method combining gated SPECT and vectorcardiography to guide left ventricular lead placement to improve response to cardiac resynchronization therapy: A proof of concept study.

BACKGROUND: The segment of the latest mechanical contraction (LMC) does not always overlap with the site of the latest electrical activation (LEA). By integrating both mechanical and electrical dyssynchrony, this proof-of-concept study aimed to propose a new method for recommending left ventricular (LV) lead placements, with the goal of enhancing response to cardiac resynchronization therapy (CRT). METHODS: The LMC segment was determined by single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) phase analysis. The LEA site was detected by vectorcardiogram. The recommended segments for LV lead placement were as follows: (1) the LMC viable segments that overlapped with the LEA site; (2) the LMC viable segments adjacent to the LEA site; (3) If no segment met either of the above, the LV lateral wall was recommended. The response was defined as ≥15% reduction in left ventricular end-systolic volume (LVESV) 6-months after CRT. Patients with LV lead located in the recommended site were assigned to the recommended group, and those located in the non-recommended site were assigned to the non-recommended group. RESULTS: The cohort comprised of 76 patients, including 54 (71.1%) in the recommended group and 22 (28.9%) in the non-recommended group. Among the recommended group, 74.1% of the patients responded to CRT, while 36.4% in the non-recommended group were responders (P = .002). Compared to pacing at the non-recommended segments, pacing at the recommended segments showed an independent association with an increased response by univariate and multivariable analysis (odds ratio 5.00, 95% confidence interval 1.73-14.44, P = .003; odds ratio 7.33, 95% confidence interval 1.53-35.14, P = .013). Kaplan-Meier curves showed that pacing at the recommended LV lead position demonstrated a better long-term prognosis. CONCLUSION: Our findings indicate that pacing at the recommended segments, by integrating of mechanical and electrical dyssynchrony, is significantly associated with an improved CRT response and better long-term prognosis.

A meta-analysis of predictive value of blood biomarkers in gestational trophoblastic neoplasia.

Background: Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been reported to play a diagnostic and predictive role in gestational trophoblastic disease. However, the conclusions are still ambiguous. This meta-analysis aimed to evaluate the combined predictive value of NLR and PLR in the malignant progression of gestational trophoblastic disease. Method: Electronic databases including PubMed, Embase, the Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure, Wanfang and China Biomedical Literature Database were searched for the relevant literature published up to 1 October 2022. Study selection and data extraction were performed independently by two reviewers. All analyses were performed using Revman, MetaDisc and STATA software. Results: A total of 858 patients from five studies were included in this meta-analysis. The pooled sensitivity and specificity of NLR were 0.8 (95% CI: 0.71-0.88) and 0.73 (95% CI: 0.69-0.76), respectively, and the area under curve of the summary receiver operating curve was 0.81. The pooled sensitivity and specificity of PLR were 0.87 (95% CI: 0.75-0.95) and 0.49 (95% CI: 0.44-0.54), respectively, and the area under curve of the summary receiver operating curve was 0.88. I2 statistic and Deek's funnel plot showed no heterogeneity and publication bias. Conclusion: NLR can accurately predict the progression from hydatidiform mole to gestational trophoblastic neoplasia and is a promising biomarker in further follow-up.

Incremental Value of Radiomics Features of Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection.

INTRODUCTION: Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, existing detection methods for COVID-19 severity assessment often lack consideration of organs and tissues other than the lungs, which limits the accuracy and reliability of these predictive models. MATERIAL AND METHODS: The retrospective study included data from 515 COVID-19 patients (Cohort 1, n=415; Cohort 2, n=100) from two centers (Shanghai Public Health Center and Brazil Niteroi Hospital) between January 2020 and July 2020. Firstly, a three-stage EAT segmentation method was proposed by combining object detection and segmentation networks. Lung and EAT radiomics features were then extracted, and feature selection was performed. Finally, a hybrid model, based on seven machine learning models, was built for detecting COVID-19 severity. The hybrid model's performance and uncertainty were evaluated in both internal and external validation cohorts. RESULTS: For EAT extraction, the Dice similarity coefficients (DSC) of the two centers were 0.972 (±0.011) and 0.968 (±0.005), respectively. For severity detection, the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) of the hybrid model increased by 0.09 (p<0.001), 19.3 % (p<0.05), and 18.0 % (p<0.05) in the internal validation cohort, and by 0.06 (p<0.001), 18.0 % (p<0.05) and 18.0 % (p<0.05) in the external validation cohort, respectively. Uncertainty and radiomics features analysis confirmed the interpretability of increased certainty in case prediction after inclusion of EAT features. CONCLUSION: This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.

Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.

PURPOSE: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves' orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. MATERIALS AND METHODS: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). RESULTS: The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. CONCLUSION: The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.

PURPOSE: Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients' response to CRT. METHODS: This analysis included 153 patients who met criteria for CRT from a prospective cohort study. The variables were used to model predictive methods for CRT. Patients were classified as "responders" for an increase of LVEF ≥ 5% at follow-up. In a second analysis, patients were classified as "super-responders" for an increase of LVEF ≥ 15%. For ML, variable selection was applied, and Prediction Analysis of Microarrays (PAM) approach was used to model response while Naïve Bayes (NB) was used to model super-response. These ML models were compared to models obtained with guideline variables. RESULTS: PAM had AUC of 0.80 against 0.72 of partial least squares-discriminant analysis with guideline variables (p = 0.52). The sensitivity (0.86) and specificity (0.75) were better than for guideline alone, sensitivity (0.75) and specificity (0.24). Neural network with guideline variables was better than NB (AUC = 0.93 vs. 0.87) however without statistical significance (p = 0.48). Its sensitivity and specificity (1.0 and 0.75, respectively) were better than guideline alone (0.78 and 0.25, respectively). CONCLUSIONS: Compared to guideline criteria, ML methods trended toward improved CRT response and super-response prediction. GMPS was central in the acquisition of most parameters. Further studies are needed to validate the models.

A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac resynchronization therapy (CRT) response. The purpose of this study is to discover new predictors from the polarmaps of LVMD by deep learning to help select heart failure patients with a high likelihood of response to CRT. METHODS: One hundred and fifty-seven patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at 6 [Formula: see text] 1 month follow up. The autoencoder (AE) technique, an unsupervised deep learning method, was applied to the polarmaps of LVMD to extract new predictors characterizing LVMD. Pearson correlation analysis was used to explain the relationships between new predictors and existing clinical parameters. Patients from the IAEA VISION-CRT trial were used for an external validation. Heatmaps were used to interpret the AE-extracted feature. RESULTS: Complete data were obtained in 130 patients, and 68.5% of them were classified as CRT responders. After variable selection by feature importance ranking and correlation analysis, one AE-extracted LVMD predictor was included in the statistical analysis. This new AE-extracted LVMD predictor showed statistical significance in the univariate (OR 2.00, P = .026) and multivariate (OR 1.11, P = .021) analyses, respectively. Moreover, the new AE-extracted LVMD predictor not only had incremental value over PBW and significant clinical variables, including QRS duration and left ventricular end-systolic volume (AUC 0.74 vs 0.72, LH 7.33, P = .007), but also showed encouraging predictive value in the 165 patients from the IAEA VISION-CRT trial (P < .1). The heatmaps for calculation of the AE-extracted predictor showed higher weights on the anterior, lateral, and inferior myocardial walls, which are recommended as LV pacing sites in clinical practice. CONCLUSIONS: AE techniques have significant value in the discovery of new clinical predictors. The new AE-extracted LVMD predictor extracted from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.

Automatic reorientation by deep learning to generate short-axis SPECT myocardial perfusion images.

BACKGROUND: Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. METHODS: A total of 254 patients were enrolled, including 226 stress SPECT MPIs and 247 rest SPECT MPIs. Fivefold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced nuclear cardiologist and used as the reference standard. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: (1) optimize the translation parameters while fixing the rotation parameters; (2) optimize rotation parameters while fixing the translation parameters; (3) optimize both translation and rotation parameters together. RESULTS: In the test set, the Spearman determination coefficients of the translation distances and rotation angles between the model prediction and the reference standard were 0.993 in X axis, 0.992 in Y axis, 0.994 in Z axis, 0.987 along X axis, 0.990 along Y axis and 0.996 along Z axis, respectively. For the 46 stress MPIs in the test set, the Spearman determination coefficients were 0.858 in percentage of profusion defect (PPD) and 0.858 in summed stress score (SSS); for the 46 rest MPIs in the test set, the Spearman determination coefficients were 0.9 in PPD and 0.9 in summed rest score (SRS). CONCLUSIONS: Our deep learning-based LV reorientation method is able to accurately generate the SA images. Technical validations and subsequent evaluations of measured clinical parameters show that it has great promise for clinical use.

Texture analysis of SPECT myocardial perfusion provides prognostic value for dilated cardiomyopathy.

BACKGROUND: Texture analysis (TA) has demonstrated clinical values in extracting information, quantifying inhomogeneity, evaluating treatment outcomes, and predicting long-term prognosis for cardiac diseases. The aim of this study was to explore whether TA of SPECT myocardial perfusion could contribute to improving the prognosis of dilated cardiomyopathy (DCM) patients. METHODS: Eighty-eight patients were recruited in our study between 2009 and 2020 who were diagnosed with DCM and underwent single-photon emission tomography myocardial perfusion imaging (SPECT MPI). Forty TA features were obtained from quantitative analysis of SPECT imaging in subjects with myocardial perfusion at rest. All patients were divided into two groups: the all-cause death group and the survival group. The prognostic value of texture parameters was assessed by Cox regression and Kaplan-Meier analysis. RESULTS: Twenty-five all-cause deaths (28.4%) were observed during the follow-up (39.2±28.7 months). Compared with the survival group, NT-proBNP and total perfusion deficit (TPD) were higher and left ventricular ejection fraction (LVEF) was lower in the all-cause death group. In addition, 26 out of 40 texture parameters were significantly different between the two groups. Univariate Cox regression analysis revealed that NT-proBNP, LVEF, and 25 texture parameters were significantly associated with all-cause death. The multivariate Cox regression analysis showed that low gray-level emphasis (LGLE) (P = 0.010, HR = 4.698, 95% CI 1.457-15.145) and long-run low gray-level emphasis (LRLGE) (P =0.002, HR = 6.085, 95% CI 1.906-19.422) were independent predictors of the survival outcome. When added to clinical parameters, LVEF, TPD, and TA parameters, including LGLE and LRLGE, were incrementally associated with all-cause death (global chi-square statistic of 26.246 vs. 33.521; P = 0.028, global chi-square statistic of 26.246 vs. 34.711; P = 0.004). CONCLUSION: TA based on gated SPECT MPI could discover independent prognostic predictors of all-cause death in medically treated patients with DCM. Moreover, TA parameters, including LGLE and LRLGE, independent of the total perfusion deficit of the cardiac myocardium, appeared to provide incremental prognostic value for DCM patients.

Identification of circRNAs and circRNA-mRNA network of Epinephelus coioides during Singapore grouper iridovirus infection.

Circular RNA (circRNA), one of the important non-coding RNA molecules with a closed-loop structure, plays a key regulatory role in cell processing. In this study, circRNAs of Epinephelus coioides, an important marine cultured fish in China, were isolated and characterized, and the network of circRNAs and mRNA was explored during Singapore grouper iridovirus (SGIV) infection, one of the most important double stranded DNA virus pathogens of marine fish. 10 g of raw data was obtained by high-throughput sequencing, and 2599 circRNAs were classified. During SGIV infection, 123 and 37 circRNAs occurred differential expression in spleen and spleen cells, indicating that circRNAs would be involved in the viral infection. GO annotation and KEGG demonstrated that circRNAs could target E. coioides genes to regulate cell activity and the activation of immune factors. The results provide some insights into the circRNAs mediated immune regulatory network during bony fish virus infection.

AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms.

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in computer-aided coronary artery disease (CAD) diagnosis. However, separating and identifying individual coronary arterial segments is challenging because morphological similarities of different branches on the coronary arterial tree and human-to-human variabilities exist. Inspired by the training procedure of interventional cardiologists for interpreting the structure of coronary arteries, we propose an association graph-based graph matching network (AGMN) for coronary arterial semantic labeling. We first extract the vascular tree from invasive coronary angiography (ICA) and convert it into multiple individual graphs. Then, an association graph is constructed from two individual graphs where each vertex represents the relationship between two arterial segments. Thus, we convert the arterial segment labeling task into a vertex classification task; ultimately, the semantic artery labeling becomes equivalent to identifying the artery-to-artery correspondence on graphs. More specifically, the AGMN extracts the vertex features by the embedding module using the association graph, aggregates the features from adjacent vertices and edges by graph convolution network, and decodes the features to generate the semantic mappings between arteries. By learning the mapping of arterial branches between two individual graphs, the unlabeled arterial segments are classified by the labeled segments to achieve semantic labeling. A dataset containing 263 ICAs was employed to train and validate the proposed model, and a five-fold cross-validation scheme was performed. Our AGMN model achieved an average accuracy of 0.8264, an average precision of 0.8276, an average recall of 0.8264, and an average F1-score of 0.8262, which significantly outperformed existing coronary artery semantic labeling methods. In conclusion, we have developed and validated a new algorithm with high accuracy, interpretability, and robustness for coronary artery semantic labeling on ICAs.

3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention.

BACKGROUND: Percutaneous coronary intervention (PCI) in stable coronary artery disease (CAD) is commonly triggered by abnormal myocardial perfusion imaging (MPI). However, due to the possibilities of multivessel disease, serial stenoses and variability of coronary artery perfusion distribution, an opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a multi-modality fusion approach to assist decision-making for PCI. METHODS AND RESULTS: Coronary arteries from fluoroscopic angiography (FA) were reconstructed into 3D artery anatomy. Left ventricular (LV) epicardial surface was extracted from SPECT. The artery anatomy and epicardial surface were non-rigidly fused. The accuracy of the 3D fusion was evaluated via both computer simulation and real patient data. Simulated FA and MPI were integrated and then compared with the ground truth from a digital phantom. The distance-based mismatch errors between simulated fluoroscopy and phantom arteries were 1.86 ± 1.43 mm for left coronary arteries (LCA) and 2.21 ± 2.50 mm for right coronary arteries (RCA). FA and SPECT images in 30 patients were integrated and then compared with the ground truth from CT angiograms. The distance-based mismatch errors between the fluoroscopy and CT arteries were 3.84 ± 3.15 mm for LCA and 5.55 ± 3.64 mm for RCA. The presence of the corresponding fluoroscopy and CT arteries in the AHA-17-segment model agreed well with a Kappa value of 0.91 (CI 0.89-0.93) for LCA and a Kappa value of 0.80 (CI 0.67-0.92) for RCA. CONCLUSIONS: Our fusion approach is technically accurate to assist PCI decision-making and is clinically feasible to be used in the catheterization laboratory. Future studies are necessary to determine if fusion improves PCI-related outcomes.

Predictive values of left ventricular mechanical dyssynchrony for CRT response in heart failure patients with different pathophysiology.

BACKGROUND: Cardiac resynchronization therapy (CRT) patients with different pathophysiology may influence mechanical dyssynchrony and get different ventricular resynchronization and clinical outcomes. METHODS: Ninety-two dilated cardiomyopathy (DCM) and fifty ischemic cardiomyopathy (ICM) patients with gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) were included in this retrospective study. Patients were classified based on the concordance between the left ventricular (LV) lead and the latest contraction or relaxation position. If the LV lead was located on or adjacent to both the latest contraction and relaxation position, the patient was categorized into the both match group; if the LV lead was located on or adjacent to the latest contraction or relaxation position, the patient was classified into the one match group; if the LV lead was located on or adjacent to neither the latest contraction nor relaxation position, the patient was categorized to the neither group. CRT response was defined as [Formula: see text] improvement of LV ejection fraction at the 6-month follow-up. Variables with P < .05 in the univariate analysis were included in the stepwise multivariate model. RESULTS: During the follow-up period, 58.7% (54 of 92) for DCM patients and 54% (27 of 50) for ICM patients were CRT responders. The univariate analysis and stepwise multivariate analysis showed that QRS duration, systolic phase bandwidth (PBW), diastolic PBW, diastolic phase histogram standard deviation (PSD), and left ventricular mechanical dyssynchrony (LVMD) concordance were independent predictors of CRT response in DCM patients; diabetes mellitus and left ventricular end-systolic volume were significantly associated with CRT response in ICM patients. The intra-group comparison revealed that the CRT response rate was significantly different in the both match group of DCM (N = 18, 94%) and ICM (N = 24, 62%) patients (P = .016). However, there was no significant difference between DCM and ICM in the one match and neither group. For the inter-group comparison, Kruskal-Wallis H-test revealed that CRT response was significantly different in all the groups of DCM patients (P < .001), but not in ICM patients (P = .383). CONCLUSIONS: Compared with ICM patients, systolic PBW, diastolic PBW and PSD have better predictive and prognostic values for the CRT response in DCM patients. Placing the LV lead in or adjacent to the latest contraction and relaxation position can improve the clinical outcomes of DCM patients, but it does not apply to ICM patients.

Incremental value of left ventricular shape parameters measured by gated SPECT MPI in predicting the super-response to CRT.

BACKGROUND: The purpose of this study was to evaluate the predictive value of left ventricular (LV) shape parameters measured by gated SPECT myocardial perfusion imaging (MPI) in super-responders enrolled in the VISION-CRT trial. METHODS: One hundred and ninety-nine patients who met standard criteria for CRT from multiple centers were enrolled in this study. End-systolic eccentricity (ESE) and end-diastolic eccentricity (EDE) were measures of LV shape. Super-responders were the patients who had a relative increase in left ventricular ejection fraction (LVEF) ≥ 15%. RESULTS: Complete data were obtained in 165 patients, and 43.6% of them were classified as super-responders. ESE was an independent predictor of CRT super-responders in univariate (OR 12.59, 95% CI 1.56-101.35, P = .017) and multivariate analysis (OR 35.71, 95% CI 1.66-766.03, P = .006). ESE had an incremental value over significant clinical and SPECT imaging variables, including angiotensin-converting enzyme inhibitors or angiotensin II receptor blocker, coronary artery disease, myocardial infarction, LVEF, end-diastolic volume index, and scar burden (AUC 0.82 vs. 0.80, sensitivity 0.68 vs. 0.65, specificity 0.82 vs. 0.78). CONCLUSIONS: LV shape parameters derived from gated SPECT MPI have the promise to improve the prediction of the super-response to CRT. Moreover, ESE provides incremental value over existing clinical and nuclear imaging variables.

Three-dimensional Fusion of Myocardial Perfusion SPECT and Invasive Coronary Angiography Guides Coronary Revascularization.

BACKGROUND: SPECT myocardial perfusion imaging (SPECT MPI) and invasive coronary angiography (ICA) provide complementary clinical information in the diagnosis of coronary artery disease (CAD). We have developed an approach for 3D fusion of perfusion data from SPECT MPI and coronary anatomy from ICA. In this study, we aimed to evaluate its clinical value when compared to the traditional side-by-side readings. METHODS: Thirty-six CAD patients who had at least one stenosis ≥ 50% were retrospectively enrolled. Based on the presence of a perfusion defect in a territory subtended by a coronary vessel, all vessels were classified as matched, unmatched, or normal groups via both the fusion and side-by-side analysis. The treatments recommended by the fusion and side-by-side analysis were compared with those that the patients received. Major adverse cardiac events (MACE), defined as all-cause death, myocardial infarction, unstable angina requiring hospitalization, and unplanned revascularization, were assessed. RESULTS: The overall vessel-based concordance was 78.7% between the fusion and side-by-side analysis. Compared with the side-by-side analysis, 23 coronary arteries (29 equivocal segments) of 19 patients were reclassified via fusion of data. In the matched, unmatched, and normal groups, the numbers of vessels with hemodynamically significant stenosis which caused reversible defect were 37 vs 53, 28 vs 14, and 43 vs 41 (P < .01) when comparing the side-by-side analysis with the fusion, and the revascularization ratios per vessel were 69% vs 88%, 29% vs 10%, and 2% vs 2% between them. During the five-year follow-up, 8 patients (22.2%) experienced MACE. Patients who received the same treatment as the guidance of 3D fusion results (n = 22) had superior outcomes when compared with those who did not (n = 14) (P < .01). CONCLUSIONS: Compared with the side-by-side analysis, the 3D fusion of SPECT MPI and ICA provided incremental diagnostic and prognostic value.

Clinical impacts of scar reduction on gated myocardial perfusion SPECT after cardiac resynchronization therapy.

BACKGROUND: It had not been reported that myocardial scar shown on gated myocardial perfusion SPECT (GMPS) might reduce after cardiac resynchronization therapy (CRT). In this study, we aim to investigate the clinical impact and characteristic of scar reduction (SR) after CRT. METHODS AND RESULTS: Sixty-one heart failure patients following standard indication for CRT received twice GMPS as pre- and post-CRT evaluations. The patients with an absolute reduction of scar ≥ 10% after CRT were classified as the SR group while the rest were classified as the non-SR group. The SR group (N = 22, 36%) showed more improvement on LV function (∆LVEF: 18.1 ± 12.4 vs 9.4 ± 9.9 %, P = 0.007, ∆ESV: - 91.6 ± 52.6 vs - 38.1 ± 46.5 mL, P < 0.001) and dyssynchrony (ΔPSD: - 26.19 ± 18.42 vs - 5.8 ± 23.0°, P < 0.001, Δ BW: - 128.7 ± 82.8 vs - 25.2 ± 109.0°, P < 0.001) than non-SR group (N = 39, 64%). Multivariate logistic regression analysis showed baseline QRSd (95% CI 1.019-1.100, P = 0.006) and pre-CRT Reduced Wall Thickening (RWT) (95% CI 1.016-1.173, P = 0.028) were independent predictors for the development of SR. CONCLUSION: More than one third of patients showed SR after CRT who had more post-CRT improvement on LV function and dyssynchrony than those without SR. Wider QRSd and higher RWT before CRT were related to the development of SR after CRT.