Search results
Found 30541 matches for
Maternal Cardiac Changes in Women With Obesity and Gestational Diabetes Mellitus.
OBJECTIVE: We investigated if women with gestational diabetes mellitus (GDM) in the third trimester of pregnancy exhibit adverse cardiac alterations in myocardial energetics, function, or tissue characteristics. RESEARCH DESIGN AND METHODS: Thirty-eight healthy, pregnant women and 30 women with GDM were recruited. Participants underwent phosphorus MRS and cardiovascular magnetic resonance for assessment of myocardial energetics (phosphocreatine [PCr] to ATP ratio), tissue characteristics, biventricular volumes and ejection fractions, left ventricular (LV) mass, global longitudinal shortening (GLS), and mitral in-flow E-wave to A-wave ratio. RESULTS: Participants were matched for age, gestational age, and ethnicity. The following data are reported as mean ± SD. The women with GDM had higher BMI (27 ± 4 vs. 33 ± 5 kg/m2; P = 0.0001) and systolic (115 ± 11 vs. 121 ± 13 mmHg; P = 0.04) and diastolic (72 ± 7 vs. 76 ± 9 mmHg; P = 0.04) blood pressures. There was no difference in N-terminal pro-brain natriuretic peptide concentrations between the groups. The women with GDM had lower myocardial PCr to ATP ratio (2.2 ± 0.3 vs. 1.9 ± 0.4; P < 0.0001), accompanied by lower LV end-diastolic volumes (76 ± 12 vs. 67 ± 11 mL/m2; P = 0.002) and higher LV mass (90 ± 13 vs. 103 ± 18 g; P = 0.001). Although ventricular ejection fractions were similar, the GLS was reduced in women with GDM (-20% ± 3% vs. -18% ± 3%; P = 0.008). CONCLUSIONS: Despite no prior diagnosis of diabetes, women with obesity and GDM manifest impaired myocardial contractility and higher LV mass, associated with reductions in myocardial energetics in late pregnancy compared with lean women with healthy pregnancy. These findings may aid our understanding of the long-term cardiovascular risks associated with GDM.
Non-invasive imaging of high-risk coronary plaque: the role of computed tomography and positron emission tomography.
Despite recent advances, cardiovascular disease remains the leading cause of death globally. As such, there is a need to optimise our current diagnostic and risk stratification pathways in order to better deliver individualised preventative therapies. Non-invasive imaging of coronary artery plaque can interrogate multiple aspects of coronary atherosclerotic disease, including plaque morphology, anatomy and flow. More recently, disease activity is being assessed to provide mechanistic insights into in vivo atherosclerosis biology. Molecular imaging using positron emission tomography is unique in this field, with the potential to identify specific biological processes using either bespoke or re-purposed radiotracers. This review provides an overview of non-invasive vulnerable plaque detection and molecular imaging of coronary atherosclerosis.
Role of inflammation in cardiopulmonary health effects of PM.
The relationship between increased exposure to PM and adverse cardiovascular effects is well documented in epidemiological studies. Inflammation in the lungs, caused by deposited particles, can be seen as a key process that could mediate adverse effects on the cardiovascular system. There are at least three potential pathways that could lead from pulmonary inflammation to adverse cardiovascular effects. Firstly, inflammation in the lung could lead to systemic inflammation, which is well known to be linked to sudden death from cardiovascular causes. Systemic inflammation can lead to destabilization by activation of inflammatory processes in atheromatous plaques. Secondly, inflammation can cause an imbalance in coagulation factors that favor propagation of thrombi if thrombosis is initiated. Thirdly, inflammation could affect the autonomic nervous system activity in ways that could lead to alterations in the control of heart rhythm which could culminate in fatal dysrhythmia.
The SSTARS (STeroids and Stents Against Re-Stenosis) Trial: Different stent alloys and the use of peri-procedural oral corticosteroids to prevent in-segment restenosis after percutaneous coronary intervention.
BACKGROUND: Stent design and technological modifications to allow for anti-proliferative drug elution influence restenosis rates following percutaneous coronary intervention (PCI). We aimed to investigate whether peri-procedural administration of corticosteroids or the use of thinner strut cobalt alloy stents would reduce rates of binary angiographic restenosis (BAR) after PCI. METHODS: This was a two centre, mixed single and double blinded, randomised controlled trial using a factorial design. We compared (a) the use of prednisolone to placebo, starting at least six hours pre-PCI and continued for 28days post-PCI, and (b) cobalt chromium (CoCr) to stainless steel (SS) alloy stents, in patients admitted for PCI. The primary end-point was BAR at six months. RESULTS: 315 patients (359 lesions) were randomly assigned to either placebo (n=145) or prednisolone (n=170) and SS (n=160) or CoCr (n=160). The majority (58%) presented with an ACS, 11% had diabetes and 287 (91%) completed angiographic follow up. BAR occurred in 26 cases in the placebo group (19.7%) versus 31 cases in the prednisolone group (20.0%) respectively, p=1.00. For the comparison between SS and CoCr stents, BAR occurred in 32 patients (21.6%) versus 25 patients (18.0%) respectively, p=0.46. CONCLUSION: Our study showed that treating patients with a moderately high dose of prednisolone for 28days following PCI with BMS did not reduce the incidence of BAR. In addition, we showed no significant reduction in 6month restenosis rates with stents composed of CoCr alloy compared to SS (http://www.isrctn.com/ISRCTN05886349).
Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals.
IMPORTANCE: Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression. OBJECTIVE: To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD. DESIGN, SETTING AND PARTICIPANTS: This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021. MAIN OUTCOMES AND MEASURES: Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform. RESULTS: The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not. CONCLUSIONS AND RELEVANCE: The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.
Artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary computed tomography angiography using a novel method
Background: Coronary computed tomography angiography (CCTA) allows non-invasive assessment of luminal stenosis and coronary atherosclerotic plaque. We aimed to develop and externally validate an artificial intelligence-based deep learning (DL) network for CCTA-based measures of plaque volume and stenosis severity. Methods: This was an international multicenter study of 1,183 patients undergoing CCTA at 11 sites. A novel DL convolutional neural network was trained to segment coronary plaque in 921 patients (5,045 lesions). The DL architecture consisted of a novel hierarchical convolutional long short-term memory (ConvLSTM) Network. The training set was further split temporally into training (80%) and internal validation (20%) datasets. Each coronary lesion was assessed in a 3D slab about the vessel centrelines. Following training and internal validation, the model was applied to an independent test set of 262 patients (1,469 lesions), which included an external validation cohort of 162 patients Results: In the test set, there was excellent agreement between DL and clinician expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.964) and percent diameter stenosis (ICC 0.879; both p<0.001, see tables and figure). The average per-patient DL plaque analysis time was 5.7 seconds versus 25-30 minutes taken by experts. There was significantly higher overlap measured by the Dice coefficient (DC) for ConvLSTM compared to UNet (DC for vessel 0.94 vs 0.83, p<0.0001; DC for lumen and plaque 0.90 vs 0.83, p<0.0001) or DeepLabv3 (DC for vessel both 0.94; DC for lumen and plaque 0.89 vs 0.84, p<0.0001). Conclusions: A novel externally validated artificial intelligence-based network provides rapid measurements of plaque volume and stenosis severity from CCTA which agree closely with clinician expert readers.
Multimodal Cardiac Segmentation Using Disentangled Representation Learning
Magnetic Resonance (MR) protocols use several sequences to evaluate pathology and organ status. Yet, despite recent advances, the analysis of each sequence’s images (modality hereafter) is treated in isolation. We propose a method suitable for multimodal and multi-input learning and analysis, that disentangles anatomical and imaging factors, and combines anatomical content across the modalities to extract more accurate segmentation masks. Mis-registrations between the inputs are handled with a Spatial Transformer Network, which non-linearly aligns the (now intensity-invariant) anatomical factors. We demonstrate applications in Late Gadolinium Enhanced (LGE) and cine MRI segmentation. We show that multi-input outperforms single-input models, and that we can train a (semi-supervised) model with few (or no) annotations for one of the modalities. Code is available at https://github.com/agis85/multimodal_segmentation.
TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant model based on the deformation-invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data
Deep convolutional neural networks (CNNs) have achieved state-of-the-art performances for multi-class segmentation of medical images. However, a common problem when dealing with large, high resolution 3D data is that the volumes input into the deep CNNs has to be either cropped or downsampled due to limited memory capacity of computing devices. These operations can lead to loss of resolution and class imbalance in the input data batches, thus downgrade the performances of segmentation algorithms. Inspired by the architecture of image super-resolution CNN (SRCNN), we propose a two-stage modified U-Net framework that simultaneously learns to detect a ROI within the full volume and to classify voxels without losing the original resolution. Experiments on a variety of multi-modal 3D cardiac images have demonstrated that this framework shows better segmentation performances than state-of-the-art Deep CNNs with trained with the same similarity metrics.
Factorised spatial representation learning: Application in semi-supervised myocardial segmentation
The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method’s utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear deformations has been evaluated through comparison experiments performed on a multi-sequence brain MR dataset and a multi-modality abdominal dataset. Our method has displayed its ability to generate synthesized data that is aligned with the source while maintaining a proper quality of signal compared to CycleGAN-generated data. The proposed model also obtained comparable performance with CycleGAN when data from the source and target domains are alignable through simple affine transformations.
Associations Between Physical Activity and Adolescent Idiopathic Scoliosis: A Systematic Review and Meta-analysis.
OBJECTIVE: To investigate the associations between adolescent idiopathic scoliosis (AIS) and physical activity (PA). DATA SOURCES: MEDLINE, EMBASE, AMED, SPORTDiscus, Cochrane Library, and CINAHL electronic databases were searched from inception to August 2022/plus citation tracking. STUDY SELECTION: Observational studies of participants with radiographically confirmed AIS with ≥10° lateral spinal curvature (Cobb method) and comparator groups without AIS that measured PA were selected by 2 reviewers. DATA EXTRACTION: Data were extracted independently and cross-checked by 2 reviewers. Risk of bias was evaluated using Newcastle Ottawa Scales and overall confidence in the evidence using the GRADE approach. DATA SYNTHESIS: Sixteen studies with 9627 participants (9162, 95% women) were included. A history of vigorous PA significantly reduced the odds of being newly diagnosed with AIS by 24% (odds ratio [OR] 0.76, 95% confidence interval [CI] 0.65-0.89) (high certainty). Moderate PA reduced odds by 13% (moderate certainty) and light PA increased odds by 9% (low certainty), but neither analysis was statistically significant. Ballet or gymnastics (OR 1.47, 95% CI 3.08 (1.90, 5.00) were the only individual sports significantly associated with AIS diagnosis (moderate certainty). Case-control studies of people with and without AIS provided greater evidence that having AIS reduces vigorous PA and sports participation, and less evidence light PA and walking are affected. CONCLUSION: Adolescents who participate in more vigorous PA are less likely to be diagnosed with AIS. Ballet and gymnastics are associated with AIS diagnosis, but the direction of this association is uncertain. People with AIS are likely to do less vigorous physical and sporting activity compared with those without AIS, which could negatively affect health and quality of life. Further research is warranted into the inter-relations between PA and AIS, studies need to be of sufficient size, include men, and evaluate vigorous including higher-impact PA compared with moderate or light PA.
The experience of women reporting damage from vaginal mesh: a reflexive thematic analysis.
BACKGROUND: The UK's 'First do no harm' report highlighted missed opportunities to prevent harm and emphasised the need to incorporate patient voices into healthcare. Due to concerns about, and the subsequent suspension, of vaginal mesh for urinary incontinence thousands of women face the decision about mesh removal surgery. The aim of this study was to explore and understand the experience of living with complications attributed to vaginal mesh surgery so that this knowledge can contribute to improvements in care for those considering mesh, or mesh removal, surgery. METHODS: This study was embedded in the 'PURSUE' study which explored the experiences of 74 people with urogynaecological conditions in the UK (30th April 2021-17th December 2021). Of these 74 people, fifteen women reported complications that they attributed to vaginal mesh surgery. We used the six stages of reflexive thematic analysis to conceptualise these fifteen accounts. FINDINGS: Our conceptual model anchors eight themes around two dualities: (1) body parts versus body whole, (2) dominant discourse versus marginal discourse. Our themes indicate that trust can be established through: (1) embodied healthcare that focuses on connecting with patients' lived experience, (2) dialectic communication that recognises patient experiences and remains open to alternative perspectives. INTERPRETATION: This study raises some important issues for education and practice. Our findings can translate to other health settings where treatments aimed to provide care have caused harm. FUNDING: NIHR Policy Research Programme (NIHR202450).