Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence.

Background: Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives: The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores. Results: Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]). Conclusions: An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.

The genome sequence of the Large Sharp-tail Bee, Coelioxys conoideus (Illiger,1806).

We present a genome assembly from an individual female Coelioxys conoideus (the Large Sharp-tail Bee; Arthropoda; Insecta; Hymenoptera; Megachilidae). The genome sequence is 417.6 megabases in span. Most of the assembly is scaffolded into 12 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 20.8 kilobases in length.

The genome sequence of the slender grass hoverfly, Melanostoma scalare (Fabricius, 1794).

We present a genome assembly from an individual male Melanostoma scalare (the slender grass hoverfly; Arthropoda; Insecta; Diptera; Syrphidae). The genome sequence is 738.2 megabases in span. Most of the assembly is scaffolded into 5 chromosomal pseudomolecules, including the X and Y sex chromosomes. The mitochondrial genome has also been assembled and is 16.08 kilobases in length.

The genome sequence of the Common Spotted Hoverfly, Eupeodes luniger (Meigen, 1822).

We present a genome assembly from an individual female Eupeodes luniger (the Common Spotted Hoverfly; Arthropoda; Insecta; Diptera; Syrphidae). The genome sequence is 616.9 megabases in span. Most of the assembly is scaffolded into 4 chromosomal pseudomolecules. The mitochondrial genome has also been assembled and is 17.45 kilobases in length.

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography.

Aims: To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results: SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and -17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69-0.87) to 0.83 (0.74-0.91) or 0.84 (0.75-0.92), respectively. Conclusion: AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.

Regulation of cardiomyocyte adhesion and mechanosignalling through distinct nanoscale behaviour of integrin ligands mimicking healthy or fibrotic extracellular matrix.

The stiffness of the cardiovascular environment changes during ageing and in disease and contributes to disease incidence and progression. Changing collagen expression and cross-linking regulate the rigidity of the cardiac extracellular matrix (ECM). Additionally, basal lamina glycoproteins, especially laminin and fibronectin regulate cardiomyocyte adhesion formation, mechanics and mechanosignalling. Laminin is abundant in the healthy heart, but fibronectin is increasingly expressed in the fibrotic heart. ECM receptors are co-regulated with the changing ECM. Owing to differences in integrin dynamics, clustering and downstream adhesion formation this is expected to ultimately influence cardiomyocyte mechanosignalling; however, details remain elusive. Here, we sought to investigate how different cardiomyocyte integrin/ligand combinations affect adhesion formation, traction forces and mechanosignalling, using a combination of uniformly coated surfaces with defined stiffness, polydimethylsiloxane nanopillars, micropatterning and specifically designed bionanoarrays for precise ligand presentation. Thereby we found that the adhesion nanoscale organization, signalling and traction force generation of neonatal rat cardiomyocytes (which express both laminin and fibronectin binding integrins) are strongly dependent on the integrin/ligand combination. Together our data indicate that the presence of fibronectin in combination with the enhanced stiffness in fibrotic areas will strongly impact on the cardiomyocyte behaviour and influence disease progression. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.

Automated analysis of limited echocardiograms: Feasibility and relationship to outcomes in COVID-19.

Background: As automated echocardiographic analysis is increasingly utilized, continued evaluation within hospital settings is important to further understand its potential value. The importance of cardiac involvement in patients hospitalized with COVID-19 provides an opportunity to evaluate the feasibility and clinical relevance of automated analysis applied to limited echocardiograms. Methods: In this multisite US cohort, the feasibility of automated AI analysis was evaluated on 558 limited echocardiograms in patients hospitalized with COVID-19. Reliability of automated assessment of left ventricular (LV) volumes, ejection fraction (EF), and LV longitudinal strain (LS) was assessed against clinically obtained measures and echocardiographic findings. Automated measures were evaluated against patient outcomes using ROC analysis, survival modeling, and logistic regression for the outcomes of 30-day mortality and in-hospital sequelae. Results: Feasibility of automated analysis for both LVEF and LS was 87.5% (488/558 patients). AI analysis was performed with biplane method in 300 (61.5%) and single plane apical 4- or 2-chamber analysis in 136 (27.9%) and 52 (10.7%) studies, respectively. Clinical LVEF was assessed using visual estimation in 192 (39.3%), biplane in 163 (33.4%), and single plane or linear methods in 104 (21.2%) of the 488 studies; 29 (5.9%) studies did not have clinically reported LVEF. LV LS was clinically reported in 80 (16.4%). Consistency between automated and clinical values demonstrated Pearson's R, root mean square error (RMSE) and intraclass correlation coefficient (ICC) of 0.61, 11.3% and 0.72, respectively, for LVEF; 0.73, 3.9% and 0.74, respectively for LS; 0.76, 24.4ml and 0.87, respectively, for end-diastolic volume; and 0.82, 12.8 ml, and 0.91, respectively, for end-systolic volume. Abnormal automated measures of LVEF and LS were associated with LV wall motion abnormalities, left atrial enlargement, and right ventricular dysfunction. Automated analysis was associated with outcomes, including survival. Conclusion: Automated analysis was highly feasible on limited echocardiograms using abbreviated protocols, consistent with equivalent clinically obtained metrics, and associated with echocardiographic abnormalities and patient outcomes.

The genome sequence of the dumpy grass hoverfly, Melanostoma mellinum (Linnaeus, 1758).

We present a genome assembly from an individual male Melanostoma mellinum (the dumpy grass hoverfly; Arthropoda; Insecta; Diptera; Syriphidae). The genome sequence is 731 megabases in span. The majority of the assembly (99.67%) is scaffolded into five chromosomal pseudomolecules, with the X and Y sex chromosomes assembled. The complete mitochondrial genome was also assembled and is 16.1 kilobases in length.

Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence.

OBJECTIVES: The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. BACKGROUND: Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. METHODS: An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. STUDY: How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. RESULTS: Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. CONCLUSIONS: Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

The genome sequence of the plain-faced dronefly, Eristalis arbustorum (Linnaeus, 1758).

We present a genome assembly from an individual female Eristalis arbustorum (the plain-faced dronefly; Arthropoda; Insecta; Diptera; Syriphidae). The genome sequence is 451 megabases in span. The majority of the assembly (94.71%) is scaffolded into 6 chromosomal pseudomolecules, with the X sex chromosome assembled. The complete mitochondrial genome was also assembled and is 16.0 kilobases in length.

The genome sequence of the two-banded wasp hoverfly, Chrysotoxum bicinctum (Linnaeus, 1758).

We present a genome assembly from an individual female Chrysotoxum bicinctum (the two-banded wasp hoverfly; Arthropoda; Insecta; Diptera; Syriphidae). The genome sequence is 913 megabases in span. The majority of the assembly (98.81%) is scaffolded into five chromosomal pseudomolecules, with the X sex chromosome assembled.

The genome sequence of the tapered dronefly, Eristalis pertinax (Scopoli, 1763).

We present a genome assembly from an individual male Eristalis tenax (the tapered dronefly; Arthropoda; Insecta; Diptera; Syriphidae). The genome sequence is 487 megabases in span. The majority of the assembly (95.23%) is scaffolded into seven chromosomal pseudomolecules, with the X and Y sex chromosomes assembled. The complete mitochondrial genome was also assembled and is 17.2 kilobases in length.

Probing the nanoscale organisation and multivalency of cell surface receptors: DNA origami nanoarrays for cellular studies with single-molecule control.

Nanoscale organisation of receptor ligands has become an important approach to study the clustering behaviour of cell-surface receptors. Biomimetic substrates fabricated via different nanopatterning strategies have so far been applied to investigate specific integrins and cell types, but without multivalent control. Here we use DNA origami to surpass the limits of current approaches and fabricate nanoarrays to study different cell adhesion processes, with nanoscale spatial resolution and single-molecule control. Notably, DNA nanostructures enable the display of receptor ligands in a highly customisable manner, with modifiable parameters including ligand number, ligand spacing and most importantly, multivalency. To test the adaptability and robustness of the system we combined it with focused ion beam and electron-beam lithography nanopatterning to additionally control the distance between the origami structures (i.e. receptor clusters). Moreover, we demonstrate how the platform can be used to interrogate two different biological questions: (1) the cooperative effect of integrin and growth factor receptor in cancer cell spreading, and (2) the role of integrin clustering in cardiomyocyte adhesion and maturation. Thereby we find previously unknown clustering behaviour of different integrins, further outlining the importance for such customisable platforms for future investigations of specific receptor organisation at the nanoscale.

Calponin-3 is critical for coordinated contractility of actin stress fibers.

Contractile actomyosin bundles, stress fibers, contribute to morphogenesis, migration, and mechanosensing of non-muscle cells. In addition to actin and non-muscle myosin II (NMII), stress fibers contain a large array of proteins that control their assembly, turnover, and contractility. Calponin-3 (Cnn3) is an actin-binding protein that associates with stress fibers. However, whether Cnn3 promotes stress fiber assembly, or serves as either a positive or negative regulator of their contractility has remained obscure. Here, we applied U2OS osteosarcoma cells as a model system to study the function of Cnn3. We show that Cnn3 localizes to both NMII-containing contractile ventral stress fibers and transverse arcs, as well as to non-contractile dorsal stress fibers that do not contain NMII. Fluorescence-recovery-after-photobleaching experiments revealed that Cnn3 is a dynamic component of stress fibers. Importantly, CRISPR/Cas9 knockout and RNAi knockdown studies demonstrated that Cnn3 is not essential for stress fiber assembly. However, Cnn3 depletion resulted in increased and uncoordinated contractility of stress fibers that often led to breakage of individual actomyosin bundles within the stress fiber network. Collectively these results provide evidence that Cnn3 is dispensable for the assembly of actomyosin bundles, but that it is required for controlling proper contractility of the stress fiber network.

Cardiomyocytes Sense Matrix Rigidity through a Combination of Muscle and Non-muscle Myosin Contractions.

Mechanical properties are cues for many biological processes in health or disease. In the heart, changes to the extracellular matrix composition and cross-linking result in stiffening of the cellular microenvironment during development. Moreover, myocardial infarction and cardiomyopathies lead to fibrosis and a stiffer environment, affecting cardiomyocyte behavior. Here, we identify that single cardiomyocyte adhesions sense simultaneous (fast oscillating) cardiac and (slow) non-muscle myosin contractions. Together, these lead to oscillating tension on the mechanosensitive adaptor protein talin on substrates with a stiffness of healthy adult heart tissue, compared with no tension on embryonic heart stiffness and continuous stretching on fibrotic stiffness. Moreover, we show that activation of PKC leads to the induction of cardiomyocyte hypertrophy in a stiffness-dependent way, through activation of non-muscle myosin. Finally, PKC and non-muscle myosin are upregulated at the costameres in heart disease, indicating aberrant mechanosensing as a contributing factor to long-term remodeling and heart failure.

Asymmetry in CT Scan Measures of Thigh Muscle 2 Months After Hip Fracture: The Baltimore Hip Studies.

BACKGROUND: Hip fracture is an important problem for older adults with significant functional consequences. After hip fracture, reduced muscle loading can result in muscle atrophy. METHODS: We compared thigh muscle characteristics in the fractured leg with those in the nonfractured leg in participants from the Baltimore Hip Studies 7th cohort using computed tomography (CT) scan imaging. RESULTS: At 2 months postfracture, a single 10-mm axial CT scan was obtained at the midthigh level in 47 participants (26 men and 21 women) with a mean age of 80.4 years (range 65-96), and thigh muscle cross-sectional area (CSA), CSA of intermuscular adipose tissue (IMAT), as well as mean radiological attenuation were measured. Total thigh muscle CSA was less on the side of the fracture by 9.2 cm(2) (95% CI: 5.9, 12.4 cm(2)), whereas the CSA of IMAT was greater by 2.8 cm(2) (95% CI: 1.9, 3.8 cm(2)) on the fractured side. Mean muscle attenuation was lower on the side of the fracture by 3.61 HU (95% CI: 2.99, 4.24 HU). CONCLUSIONS: The observed asymmetry is consistent with the effect of disuse and inflammation in the affected limb along with training effects in the unaffected limb due to the favoring of this leg with ambulation during the postfracture period.

Asymmetry in CT Scan Measures of Thigh Muscle 2 Months After Hip Fracture: The Baltimore Hip Studies.

BACKGROUND: Hip fracture is an important problem for older adults with significant functional consequences. After hip fracture, reduced muscle loading can result in muscle atrophy. METHODS: We compared thigh muscle characteristics in the fractured leg to those in the nonfractured leg in participants from the Baltimore Hip Studies 7th cohort using computed tomography scan imaging. RESULTS: At 2 months postfracture, a single 10mm axial computed tomography scan was obtained at the midthigh level in 43 participants (23 men, 20 women) with a mean age of 79.9 years (range: 65-96 years), and thigh muscle cross-sectional area, cross-sectional area of intermuscular adipose tissue, and mean radiologic attenuation were measured. Total thigh muscle cross-sectional area was less on the side of the fracture by 9.46cm(2) (95% CI: 5.97cm(2), 12.95cm(2)) while the cross-sectional area of intermuscular adipose tissue was greater by 2.97cm(2) (95% CI: 1.94cm(2), 4.01cm(2)) on the fractured side. Mean muscle attenuation was lower on the side of the fracture by 3.66 Hounsfield Units (95% CI: 2.98 Hounsfield Units, 4.34 Hounsfield Units). CONCLUSIONS: The observed asymmetry is consistent with the effect of disuse and inflammation in the affected limb along with training effects in the unaffected limb due to the favoring of this leg with ambulation during the postfracture period.

Body mass index is positively associated with bone mineral density in US older adults.

UNLABELLED: Literature has been conflicting as to whether obesity is protective against osteoporosis. Understanding the relationship is particularly important in light of the increasing prevalence of obesity among older adults. Study results confirm a protective association between obesity and osteoporosis in a recent, nationally representative sample of US older adults. PURPOSE: Currently, the majority of US older adults are either overweight or obese. Evidence regarding the relationship between body composition measures and bone mass is conflicting, possibly because different measures of obesity reflect multiple mechanisms. Additionally, there are important age, gender, and racial differences in a risk of osteoporosis and fat mass composition. The objective of this study was to examine the association between body mass index (BMI) and bone mineral density (BMD) in a recent, nationally representative sample of US older adults as well as to see if this relationship differs by age, sex, and race. METHODS: Data for this study were obtained from the National Health and Nutrition Examination Survey (2005-2008) for adults ages 50 and older (n = 3,296). Linear regression models were used to predict BMD of the femoral neck (measured by dual-energy X-ray absorptiometry (DXA)) as a function of BMI (measured height and weight) and a range of study covariates. RESULTS: Every unit increase in BMI was associated with an increase of 0.0082 g/cm(2) in BMD (p < 0.001). Interaction terms for BMI and age (p = 0.345), BMI and sex (p = 0.413), and BMI and race (p = 0.725) were not statistically significant. CONCLUSIONS: Study results confirm the positive association between BMI and BMD, and this relationship does not differ by age, sex, or race. A 10-unit increase in BMI (e.g., from normal BMI to obese) would result in moving an individual from an osteoporotic BMD level to a normal BMD level. Results demonstrate a protective, cross-sectional association between obesity and osteoporosis in a recent sample of US older adults.