OBJECTIVES: Emotional stress may disproportionally affect young women with ischemic heart disease. We sought to examine whether mental stress-induced myocardial ischemia (MSIMI), but not exercise-induced ischemia, is more common in young women with previous myocardial infarction (MI) than in men.
METHODS: We studied 98 post-MI patients (49 women and 49 men) aged 38 to 60 years. Women and men were matched for age, MI type, and months since MI. Patients underwent technetium-99m sestamibi perfusion imaging at rest, after mental stress, and after exercise/pharmacological stress. Perfusion defect scores were obtained with observer-independent software. A summed difference score (SDS), the difference between stress and rest scores, was used to quantify ischemia under both stress conditions.
RESULTS: Women 50 years or younger, but not older women, showed a more adverse psychosocial profile than did age-matched men but did not differ for conventional risk factors and tended to have less angiographic coronary artery disease. Compared with age-matched men, women 50 years or younger exhibited a higher SDS with mental stress (3.1 versus 1.5, p = .029) and had twice the rate of MSIMI (SDS ≥3 52% versus 25%), whereas ischemia with physical stress did not differ (36% versus 25%). In older patients, there were no sex differences in MSIMI. The higher prevalence of MSIMI in young women persisted when adjusting for sociodemographic and life-style factors, coronary artery disease severity, and depression.
CONCLUSIONS: MSIMI post-MI is more common in women 50 years or younger compared with age-matched men. These sex differences are not observed in post-MI patients who are older than 50 years.
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Jessica A Turner;
Vince Calhoun;
Paul M Thompson;
Neda Jahanshad;
Christopher RK Ching;
Sophia Thomopoulos;
Eric Verner;
Gregory P Strauss;
Anthony O Ahmed;
Matthew D Turner;
Sunitha Basodi;
Judith M Ford;
Daniel H Mathalon;
Adrian Preda;
Ayesnil Belger;
Bryon A Mueller;
Kelvin O Lim;
Theo GM van Erp
The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products Findable, Accessible, Interoperable, and Reusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTAC support the FAIR principles, and then showcase their integration with a decentralized meta-analysis of sex differences in negative symptom severity in schizophrenia, and finally present ongoing activities and plans to advance FAIR principles in ENIGMA + COINSTAC. ENIGMA and COINSTAC currently represent efforts toward improved Access, Interoperability, and Reusability. We highlight additional improvements needed in these areas, as well as future connections to other resources for expanded Findability.
Recent experimental and theoretical studies on the dynamics of the reactions of methane with F and Cl atoms have modified our understanding of mode-selective chemical reactivity. The O + methane reaction is also an important candidate to extend our knowledge on the rules of reactivity. Here, we report a unique full-dimensional ab initio potential energy surface for the O(3P) + methane reaction, which opens the door for accurate dynamics calculations using this surface. Quasiclassical trajectory calculations of the angular and vibrational distributions for the ground state and CH stretching excited O + CHD3(v1 = 0,1) → OH + CD3 reactions are in excellent agreement with the experiment. Our theory confirms what was proposed experimentally: The mechanistic origin of the vibrational enhancement is that the CH-stretching excitation enlarges the reactive cone of acceptance.
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Charles Cooke;
René R. Sevag Packard;
David C Cooke;
Kenneth F Van Train;
John Votaw;
James W Sayre;
Joel L Lazewatsky;
Kelly M Champagne;
Cesare Orlandi;
Ernest V Garcia;
Jamshid Maddahi
Background: Computerized methodologies standardize the myocardial perfusion imaging (MPI) interpretation process. Methods: To develop an automated relative perfusion quantitation approach for 18F-flurpiridaz, PET MPI studies from all phase III trial participants of 18F-flurpiridaz were divided into 3 groups. Count distributions were obtained in N = 40 normal patients undergoing pharmacological or exercise stress. Then, N = 90 additional studies were selected in a derivation group. Following receiver operating characteristic curve analysis, various standard deviations below the mean normal were used as cutoffs for significant CAD, and interobserver variability determined. Finally, diagnostic performance was compared between blinded visual readers and blinded derivations of automated relative quantitation in the remaining N = 548 validation patients. Results: Both approaches yielded comparable accuracies for the detection of global CAD, reaching 71% and 72% by visual reads, and 72% and 68% by automated relative quantitation, when using CAD ≥ 70% or ≥ 50% stenosis for significance, respectively. Similar results were observed when analyzing individual coronary territories. In both pharmacological and exercise stress, automated relative quantitation demonstrated significantly more interobserver agreement than visual reads. Conclusions: Our automated method of 18F-flurpiridaz relative perfusion analysis provides a quantitative, objective, and highly reproducible assessment of PET MPI in normal and CAD subjects undergoing either pharmacological or exercise stress.
The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.
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Ki-Bum Won;
Hyung-Bok Park;
Ran Heo;
Byoung Kwon Lee;
Fay Y Lin;
Martin Hadamitzky;
Yong-Jin Kim;
Ji Min Sung;
Edoardo Conte;
Daniele Andreini;
Gianluca Pontone;
Matthew J Budoff;
Ilan Gottlieb;
Eun Ju Chun;
Fliippo Cademartiri;
Erica Maffei;
Hugo Marques;
Pedro de Araújo Goncalves;
Jonathon A Leipsic;
Sang-Eun Lee;
Sanghoon Shin;
Jung Hyun Choi;
Renu Virmani;
Habib Samady;
Kavitha Chinnaiyan;
Daniel S Berman;
Jagat Narula;
Jeroen J Bax;
James K Min;
Hyuk-Jae Chang
Background: Atherosclerosis-related adverse events are commonly observed even in conditions with low cardiovascular (CV) risk. Longitudinal data regarding the association of normal systolic blood pressure maintenance (SBPmaintain) with coronary plaque volume changes (PVC) has been limited in adults without traditional CV disease. Hypothesis: Normal SBPmaintain is important to attenuate coronary atherosclerosis progression in adults without baseline CV disease. Methods: We analyzed 95 adults (56.7 ± 8.5 years; 40.0% men) without baseline CV disease who underwent serial coronary computed tomographic angiography with mean 3.5 years of follow-up. All participants were divided into two groups of normal SBPmaintain (follow-up SBP < 120 mm Hg) and ≥elevated SBPmaintain (follow-up SBP ≥ 120 mm Hg). Annualized PVC was defined as PVC divided by the interscan period. Results: Compared to participants with normal SBPmaintain, those with ≥elevated SBPmaintain had higher annualized total PVC (mm3/year) (0.0 [0.0–2.2] vs. 4.1 [0.0–13.0]; p <.001). Baseline total plaque volume (β =.10) and the levels of SBPmaintain (β =.23) and follow-up high-density lipoprotein cholesterol (β = −0.28) were associated with annualized total PVC (all p <.05). The optimal cutoff of SBPmaintain for predicting plaque progression was 118.5 mm Hg (sensitivity: 78.2%, specificity: 62.5%; area under curve: 0.700; 95% confidence interval [CI]: 0.59–0.81; p <.05). SBPmaintain ≥ 118.5 mm Hg (odds ratio [OR]: 4.03; 95% CI: 1.51–10.75) and baseline total plaque volume (OR: 1.03; 95% CI: 1.01–1.06) independently influenced coronary plaque progression (all p <.05). Conclusion: Normal SBPmaintain is substantial to attenuate coronary atherosclerosis progression in conditions without established CV disease.
Purpose: The computed tomography (CT)-derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion-induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT-ventilation images and single photon emission CT-ventilation (SPECT-V). Methods: Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, (Formula presented.), was conducted using the 4DCT and SPECT-V images acquired for 15 non-small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT-ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range (Formula presented.). Voxel-wise Spearman correlation between the SPECT-V and the resulting CT-ventilation images was computed. Results: The median correlations between MCVC and SPECT-V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT-V ranged between 0.79 and 0.82. For the optimal IJF tolerance (Formula presented.), the IJF and SPECT-V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance (Formula presented.), the MCVC and SPECT-V correlations across all 15 test cases ranged between −0.06 and 0.84. Conclusion: The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT-V, with the transformation-based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for (Formula presented.), indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.
Paraneoplastic syndromes are a rare clinical presentation of tumor thought to affect 0.01% of patients with cancer. Paraneoplastic syndromes present a diagnostic challenge as a wide variety of signs and symptoms may appear. This study examines the use of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) as a diagnostic imaging tool for detecting tumor in suspected paraneoplastic syndrome cases. This single-center retrospective study included patients with suspected paraneoplastic syndrome who underwent whole-body 18F-FDG PET/CT scan between December 2005 and December 2016. Associated clinical data were gathered via electronic chart review. Patient records were reviewed for age, sex, clinical signs and symptoms, ancillary diagnostic procedures, date of diagnosis, and follow-up time. Ninety-nine patients met inclusion criteria for this study. Mean follow-up period was 1.8 years. Cancer prevalence was 12.1%. The 18F-FDG PET/CT results are as follows: 10 true positives, 5 false positives, 82 true negatives, and 2 false negatives. The diagnostic values are as follows: sensitivity 83.3%, specificity 94.3%, positive predictive value 66.7%, and negative predictive value (NPV) 97.6%. The high NPV in our study supports the effectiveness of 18F-FDG PET/CT to rule out tumor in suspected paraneoplastic syndrome. Future research aims to analyze which patients with suspected paraneoplastic syndrome would benefit most from 18F-FDG PET/CT.
Numerical models of the mitral valve have been used to elucidate mitral valve function and mechanics. These models have evolved from simple two-dimensional approximations to complex three-dimensional fully coupled fluid structure interaction models. However, to date these models lack direct one-to-one experimental validation. As computational solvers vary considerably, experimental benchmark data are critically important to ensure model accuracy. In this study, a novel left heart simulator was designed specifically for the validation of numerical mitral valve models. Several distinct experimental techniques were collectively performed to resolve mitral valve geometry and hemodynamics. In particular, micro-computed tomography was used to obtain accurate and high-resolution (39 μm voxel) native valvular anatomy, which included the mitral leaflets, chordae tendinae, and papillary muscles. Three-dimensional echocardiography was used to obtain systolic leaflet geometry. Stereoscopic digital particle image velocimetry provided all three components of fluid velocity through the mitral valve, resolved every 25 ms in the cardiac cycle. A strong central filling jet (V ∼ 0.6 m/s) was observed during peak systole with minimal out-of-plane velocities. In addition, physiologic hemodynamic boundary conditions were defined and all data were synchronously acquired through a central trigger. Finally, the simulator is a precisely controlled environment, in which flow conditions and geometry can be systematically prescribed and resultant valvular function and hemodynamics assessed. Thus, this work represents the first comprehensive database of high fidelity experimental data, critical for extensive validation of mitral valve fluid structure interaction simulations.
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Wei Tse Li;
Jiayan Ma;
Neil Shende;
Grant Castaneda;
Jaideep Chakladar;
Joseph C. Tsai;
Lauren Apostol;
Christine O. Honda;
Jingyue Xu;
Lindsay M. Wong;
Tianyi Zhang;
Abby Lee;
Aditi Gnanasekar;
Thomas K. Honda;
Selena Z. Kuo;
Michael Andrew Yu;
Eric Y. Chang;
Mahadevan "Raj" Rajasekaran;
Weg M. Ongkeko
Background: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.