A Platform for Generation of Chamber-Specific Cardiac Tissues and Disease Modeling.

Tissue engineering using cardiomyocytes derived from human pluripotent stem cells holds a promise to revolutionize drug discovery, but only if limitations related to cardiac chamber specification and platform versatility can be overcome. We describe here a scalable tissue-cultivation platform that is cell source agnostic and enables drug testing under electrical pacing. The plastic platform enabled on-line noninvasive recording of passive tension, active force, contractile dynamics, and Ca2+ transients, as well as endpoint assessments of action potentials and conduction velocity. By combining directed cell differentiation with electrical field conditioning, we engineered electrophysiologically distinct atrial and ventricular tissues with chamber-specific drug responses and gene expression. We report, for the first time, engineering of heteropolar cardiac tissues containing distinct atrial and ventricular ends, and we demonstrate their spatially confined responses to serotonin and ranolazine. Uniquely, electrical conditioning for up to 8 months enabled modeling of polygenic left ventricular hypertrophy starting from patient cells.

Structural Basis of the Activation of Heterotrimeric Gs-Protein by Isoproterenol-Bound ß1-Adrenergic Receptor.

Cardiac disease remains the leading cause of morbidity and mortality worldwide. The ß1-adrenergic receptor (ß1-AR) is a major regulator of cardiac functions and is downregulated in the majority of heart failure cases. A key physiological process is the activation of heterotrimeric G-protein Gs by ß1-ARs, leading to increased heart rate and contractility. Here, we use cryo-electron microscopy and functional studies to investigate the molecular mechanism by which ß1-AR activates Gs. We find that the tilting of α5-helix breaks a hydrogen bond between the sidechain of His373 in the C-terminal α5-helix and the backbone carbonyl of Arg38 in the N-terminal αN-helix of Gαs. Together with the disruption of another interacting network involving Gln59 in the α1-helix, Ala352 in the ß6-α5 loop, and Thr355 in the α5-helix, these conformational changes might lead to the deformation of the GDP-binding pocket. Our data provide molecular insights into the activation of G-proteins by G-protein-coupled receptors.

Id genes are essential for early heart formation.

Deciphering the fundamental mechanisms controlling cardiac specification is critical for our understanding of how heart formation is initiated during embryonic development and for applying stem cell biology to regenerative medicine and disease modeling. Using systematic and unbiased functional screening approaches, we discovered that the Id family of helix-loop-helix proteins is both necessary and sufficient to direct cardiac mesoderm formation in frog embryos and human embryonic stem cells. Mechanistically, Id proteins specify cardiac cell fate by repressing two inhibitors of cardiogenic mesoderm formation-Tcf3 and Foxa2-and activating inducers Evx1, Grrp1, and Mesp1. Most importantly, CRISPR/Cas9-mediated ablation of the entire Id (Id1-4) family in mouse embryos leads to failure of anterior cardiac progenitor specification and the development of heartless embryos. Thus, Id proteins play a central and evolutionarily conserved role during heart formation and provide a novel means to efficiently produce cardiovascular progenitors for regenerative medicine and drug discovery applications.

Divergent Biochemical Properties and Disparate Impact of Arrhythmogenic Calmodulin Mutations on Zebrafish Cardiac Function.

Calmodulin (CaM) is a ubiquitous, small cytosolic calcium (Ca2+)-binding sensor that plays a vital role in many cellular processes by binding and regulating the activity of over 300 protein targets. In cardiac muscle, CaM modulates directly or indirectly the activity of several proteins that play a key role in excitation-contraction coupling (ECC), such as ryanodine receptor type 2 (RyR2), l-type Ca2+ (Cav1.2), sodium (NaV1.5) and potassium (KV7.1) channels. Many recent clinical and genetic studies have reported a series of CaM mutations in patients with life-threatening arrhythmogenic syndromes, such as long QT syndrome (LQTS) and catecholaminergic polymorphic ventricular tachycardia (CPVT). We recently showed that four arrhythmogenic CaM mutations (N98I, D132E, D134H, and Q136P) significantly reduce the binding of CaM to RyR2. Herein, we investigate in vivo functional effects of these CaM mutations on the normal zebrafish embryonic heart function by microinjecting complementary RNA corresponding to CaMN98I, CaMD132E, CaMD134H, and CaMQ136P mutants. Expression of CaMD132E and CaMD134H mutants results in significant reduction of the zebrafish heart rate, mimicking a severe form of human bradycardia, whereas expression of CaMQ136P results in an increased heart rate mimicking human ventricular tachycardia. Moreover, analysis of cardiac ventricular rhythm revealed that the CaMD132E and CaMN98I zebrafish groups display an irregular pattern of heart beating and increased amplitude in comparison to the control groups. Furthermore, circular dichroism spectroscopy experiments using recombinant CaM proteins reveals a decreased structural stability of the four mutants compared to the wild-type CaM protein in the presence of Ca2+. Finally, Ca2+-binding studies indicates that all CaM mutations display reduced CaM Ca2+-binding affinities, with CaMD132E exhibiting the most prominent change. Our data suggest that CaM mutations can trigger different arrhythmogenic phenotypes through multiple and complex molecular mechanisms.

The role of deubiquitinases in cardiac disease.

Deubiquitinases are a group of proteins that identify and digest monoubiquitin chains or polyubiquitin chains attached to substrate proteins, preventing the substrate protein from being degraded by the ubiquitin-proteasome system. Deubiquitinases regulate cellular autophagy, metabolism and oxidative stress by acting on different substrate proteins. Recent studies have revealed that deubiquitinases act as a critical regulator in various cardiac diseases, and control the onset and progression of cardiac disease through a board range of mechanism. This review summarizes the function of different deubiquitinases in cardiac disease, including cardiac hypertrophy, myocardial infarction and diabetes mellitus-related cardiac disease. Besides, this review briefly recapitulates the role of deubiquitinases modulators in cardiac disease, providing the potential therapeutic targets in the future.

Three-dimensional cardiac models: a pre-clinical testing platform.

Major advancements in human pluripotent stem cell (hPSC) technology over recent years have yielded valuable tools for cardiovascular research. Multi-cell type 3-dimensional (3D) cardiac models in particular, are providing complementary approaches to animal studies that are better representatives than simple 2-dimensional (2D) cultures of differentiated hPSCs. These human 3D cardiac models can be broadly divided into two categories; namely those generated through aggregating pre-differentiated cells and those that form self-organizing structures during their in vitro differentiation from hPSCs. These models can either replicate aspects of cardiac development or enable the examination of interactions among constituent cell types, with some of these models showing increased maturity compared with 2D systems. Both groups have already emerged as physiologically relevant pre-clinical platforms for studying heart disease mechanisms, exhibiting key functional attributes of the human heart. In this review, we describe the different cardiac organoid models derived from hPSCs, their generation methods, applications in cardiovascular disease research and use in drug screening. We also address their current limitations and challenges as pre-clinical testing platforms and propose potential improvements to enhance their efficacy in cardiac drug discovery.

Cardiogenic shock in pregnancy.

Cardiac disease complicates 1%-4% of pregnancies globally, with a predominance in low and middle-income countries (LMICs). Increasing maternal age, rates of obesity, cardiovascular comorbidities, pre-eclampsia and gestational diabetes all contribute to acquired cardiovascular disease in pregnancy. Additionally, improved survival in congenital heart disease (CHD) has led to increasing numbers of women with CHD undergoing pregnancy. Implementation of individualised care plans formulated through pre-conception counselling and based on national and international guidance have contributed to improved clinical outcomes. However, there remains a significant proportion of women of reproductive age with no apparent comorbidities or risk factors that develop heart disease during pregnancy, with no indication for pre-conception counselling. The most extreme manifestation of cardiac disease is cardiogenic shock (CS), where the primary cardiac pathology results in inadequate cardiac output and hypoperfusion, and is associated with significant mortality and morbidity. Key to management is early recognition, intervention to treat any potentially reversible underlying pathology and supportive measures, up to and including mechanical circulatory support (MCS). In this narrative review we discuss recent developments in the classification of CS, and how these may be adapted to improve outcomes of pregnant women with, or at risk of developing, this potentially lethal condition.

Cost-utility analysis of prenatal diagnosis of congenital cardiac diseases using deep learning.

BACKGROUND: Deep learning (DL) is a new technology that can assist prenatal ultrasound (US) in the detection of congenital heart disease (CHD) at the prenatal stage. Hence, an economic-epidemiologic evaluation (aka Cost-Utility Analysis) is required to assist policymakers in deciding whether to adopt the new technology. METHODS: The incremental cost-utility ratios (CUR), of adding DL assisted ultrasound (DL-US) to the current provision of US plus pulse oximetry (POX), was calculated by building a spreadsheet model that integrated demographic, economic epidemiological, health service utilization, screening performance, survival and lifetime quality of life data based on the standard formula: CUR = Increase in Intervention Costs - Decrease in Treatment costs Averted QALY losses of adding DL to US & POX US screening data were based on real-world operational routine reports (as opposed to research studies). The DL screening cost of 145 USD was based on Israeli US costs plus 20.54 USD for reading and recording screens. RESULTS: The addition of DL assisted US, which is associated with increased sensitivity (95% vs 58.1%), resulted in far fewer undiagnosed infants (16 vs 102 [or 2.9% vs 15.4%] of the 560 and 659 births, respectively). Adoption of DL-US will add 1,204 QALYs. with increased screening costs 22.5 million USD largely offset by decreased treatment costs (20.4 million USD). Therefore, the new DL-US technology is considered "very cost-effective", costing only 1,720 USD per QALY. For most performance combinations (sensitivity > 80%, specificity > 90%), the adoption of DL-US is either cost effective or very cost effective. For specificities greater than 98% (with sensitivities above 94%), DL-US (& POX) is said to "dominate" US (& POX) by providing more QALYs at a lower cost. CONCLUSION: Our exploratory CUA calculations indicate the feasibility of DL-US as being at least cost-effective.

Psychological interventions targeting mental health and stress among females with cardiac disease: a scoping review.

Interventions that target mental health symptoms and stress among those with established cardiac disease have included predominately male samples despite female patients reporting greater severity of these symptoms. The aim of this scoping review was to synthesize the published literature on psychological interventions for females with cardiac disease. We conducted a systematic search of peer-reviewed randomized clinical trials (RCTs) published in the English language from 2003 to 2023, in three databases: Medline (Ovid), PsycInfo (Ovid), and CINAHL (EBSCO). Articles that included female samples, a control or comparison group, implemented psychological interventions, and measured depression, anxiety, or stress as an outcome were included in the review. Nine articles describing eight RCTs of psychological interventions, with a total of 1587 female patients with cardiac disease, were included. Interventions were most successful at reducing stress (75% of studies measuring stress reported efficacy), while symptoms of depression and anxiety were less responsive to intervention (∼30% of studies targeting these symptoms reported improvements) in comparison to a control condition. This scoping review highlights that further advancement in knowledge is required to better address the needs of females with cardiac disease and distress, particularly depression and anxiety.

Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed nations. Therefore, the objective of the study was to assess the prevalence of death due to cardiac disease and its risk factors among heart patients in Ethiopia. METHODS: The current investigation included all cardiac patients who had cardiac surgery in the country between 2012 and 2023. A total of 1520 individuals were participated in the study. Data collection took place between February 2022 and January 2023. The study design was a retrospective cohort since the study track back patients' chart since 2012. Machine learning algorithms were applied for data analysis. For machine learning algorithms comparison, lift and AUC was applied. RESULTS: From all possible algorithms, logistic algorithm at 90%/10% was the best fit since it produces the maximum AUC value. In addition, based on the lift value of 3.33, it can be concluded that the logistic regression algorithm was performing well and providing substantial improvement over random selection. From the logistic regression machine learning algorithms, age, saturated oxygen, ejection fraction, duration of cardiac center stays after surgery, waiting time to surgery, hemoglobin, and creatinine were significant predictors of death. CONCLUSION: Some of the predictors for the death of cardiac disease patients are identified as such special attention should be given to aged patients, for patients waiting for long periods of time to get surgery, lower saturated oxygen, higher creatinine value, lower ejection fraction and for patients with lower hemoglobin values.

Melatonin and Aging.

The health problems associated with the aging process are becoming increasingly widespread due to the increase in mean life expectancy taking place globally. While decline of many organ functions is an unavoidable concomitant of senescence, these can be delayed or moderated by a range of factors. Among these are dietary changes and weight control, taking sufficient exercise, and the utilization of various micronutrients. The utility of incurring appropriate changes in lifestyle is generally not confined to a single organ system but has a broadly positive systemic effect.Among one of the most potent means of slowing down age-related changes is the use of melatonin, a widely distributed biological indole. While melatonin is well known as a treatment for insomnia, it has a wide range of beneficial qualities many of which are relevant. This overview describes how several of the properties of melatonin are especially relevant to many of the changes associated with senescence. Changes in functioning of the immune system are particularly marked in the aged, combining diminishing effectiveness with increasing ineffective and harmful activity. Melatonin treatment appears able to moderate and partially reverse this detrimental drift toward immune incompetence.

Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.

BACKGROUND: The presence of bias in artificial intelligence has garnered increased attention, with inequities in algorithmic performance being exposed across the fields of criminal justice, education, and welfare services. In health care, the inequitable performance of algorithms across demographic groups may widen health inequalities. OBJECTIVE: Here, we identify and characterize bias in cardiology algorithms, looking specifically at algorithms used in the management of heart failure. METHODS: Stage 1 involved a literature search of PubMed and Web of Science for key terms relating to cardiac machine learning (ML) algorithms. Papers that built ML models to predict cardiac disease were evaluated for their focus on demographic bias in model performance, and open-source data sets were retained for our investigation. Two open-source data sets were identified: (1) the University of California Irvine Heart Failure data set and (2) the University of California Irvine Coronary Artery Disease data set. We reproduced existing algorithms that have been reported for these data sets, tested them for sex biases in algorithm performance, and assessed a range of remediation techniques for their efficacy in reducing inequities. Particular attention was paid to the false negative rate (FNR), due to the clinical significance of underdiagnosis and missed opportunities for treatment. RESULTS: In stage 1, our literature search returned 127 papers, with 60 meeting the criteria for a full review and only 3 papers highlighting sex differences in algorithm performance. In the papers that reported sex, there was a consistent underrepresentation of female patients in the data sets. No papers investigated racial or ethnic differences. In stage 2, we reproduced algorithms reported in the literature, achieving mean accuracies of 84.24% (SD 3.51%) for data set 1 and 85.72% (SD 1.75%) for data set 2 (random forest models). For data set 1, the FNR was significantly higher for female patients in 13 out of 16 experiments, meeting the threshold of statistical significance (-17.81% to -3.37%; P<.05). A smaller disparity in the false positive rate was significant for male patients in 13 out of 16 experiments (-0.48% to +9.77%; P<.05). We observed an overprediction of disease for male patients (higher false positive rate) and an underprediction of disease for female patients (higher FNR). Sex differences in feature importance suggest that feature selection needs to be demographically tailored. CONCLUSIONS: Our research exposes a significant gap in cardiac ML research, highlighting that the underperformance of algorithms for female patients has been overlooked in the published literature. Our study quantifies sex disparities in algorithmic performance and explores several sources of bias. We found an underrepresentation of female patients in the data sets used to train algorithms, identified sex biases in model error rates, and demonstrated that a series of remediation techniques were unable to address the inequities present.

Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review.

BACKGROUND: Atrial fibrillation (AF) is a leading chronic cardiac disease associated with an increased risk of stroke, cardiac complications, and general mortality. Mobile health (mHealth) interventions, including wearable devices and apps, can aid in the detection, screening, and management of AF to improve patient outcomes. The inclusion of approaches that consider user experiences and behavior in the design of health care interventions can increase the usability of mHealth interventions, and hence, hopefully, yield an increase in positive outcomes in the lives of users. OBJECTIVE: This study aims to show how research has considered user experiences and behavioral approaches in designing mHealth interventions for AF detection, screening, and management; the phases of designing complex interventions from the UK Medical Research Council (MRC) were referenced: namely, identification, development, feasibility, evaluation, and implementation. METHODS: Studies published until September 7, 2022, that examined user experiences and behavioral approaches associated with mHealth interventions in the context of AF were extracted from multiple databases. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines were used. RESULTS: A total of 2219 records were extracted, with only 55 records reporting on usability, user experiences, or behavioral approaches more widely for designing mHealth interventions in the context of AF. When mapping the studies onto the phases of the UK MRC's guidance for developing and evaluating complex interventions, the following was found: in the identification phase, there were significant differences between the needs of patients and health care workers. In the development phase, user perspectives guided the iterative development of apps, interfaces, and intervention protocols in 4 studies. Most studies (43/55, 78%) assessed the usability of interventions in the feasibility phase as an outcome, although the data collection tools were not designed together with users and stakeholders. Studies that examined the evaluation and implementation phase entailed reporting on challenges in user participation, acceptance, and workflows that could not be captured by studies in the previous phases. To realize the envisaged human behavior intended through treatment, review results highlight the scant inclusion of behavior change approaches for mHealth interventions across multiple levels of sociotechnical health care systems. While interventions at the level of the individual (micro) and the level of communities (meso) were found in the studies reviewed, no studies were found intervening at societal levels (macro). Studies also failed to consider the temporal variation of user goals and feedback in the design of long-term behavioral interventions. CONCLUSIONS: In this systematic review, we proposed 2 contributions: first, mapping studies to different phases of the MRC framework for developing and evaluating complex interventions, and second, mapping behavioral approaches to different levels of health care systems. Finally, we discuss the wider implications of our results in guiding future mHealth research.

Post-surgery survival and associated factors for cardiac patients in Ethiopia: applications of machine learning, semi-parametric and parametric modelling.

INTRODUCTION: Living in poverty, especially in low-income countries, are more affected by cardiovascular disease. Unlike the developed countries, it remains a significant cause of preventable heart disease in the Sub-Saharan region, including Ethiopia. According to the Ethiopian Ministry of Health statement, around 40,000 cardiac patients have been waiting for surgery in Ethiopia since September 2020. There is insufficient information about long-term cardiac patients' post-survival after cardiac surgery in Ethiopia. Therefore, the main objective of the current study was to determine the long-term post-cardiac surgery patients' survival status in Ethiopia. METHODS: All patients attended from 2012 to 2023 throughout the country were included in the current study. The total number of participants was 1520 heart disease patients. The data collection procedure was conducted from February 2022- January 2023. Machine learning algorithms were applied. Gompertz regression was used also for the multivariable analysis report. RESULTS: From possible machine learning models, random survival forest were preferred. It emphasizes, the most important variable for clinical prediction was SPO2, Age, time to surgery waiting time, and creatinine value and it accounts, 42.55%, 25.17%,11.82%, and 12.19% respectively. From the Gompertz regression, lower saturated oxygen, higher age, lower ejection fraction, short period of cardiac center stays after surgery, prolonged waiting time to surgery, and creating value were statistically significant predictors of death outcome for post-cardiac surgery patients' survival in Ethiopia. CONCLUSION: Some of the risk factors for the death of post-cardiac surgery patients are identified in the current investigation. Particular attention should be given to patients with prolonged waiting times and aged patients. Since there were only two fully active cardiac centers in Ethiopia it is far from an adequate number of centers for more than 120 million population, therefore, the study highly recommended to increase the number of cardiac centers that serve as cardiac surgery in Ethiopia.

Cardiogenic Necrotizing Enterocolitis in Infants with Congenital Heart Disease: A Systematic Review and Meta-analysis.

Necrotizing enterocolitis (NEC) is a known cause of morbidity and mortality in infants with congenital heart disease (CHD), but reports about the burden of cardiogenic NEC frequently conflict. To synthesize the extant literature on the incidence, risk factors, and prognosis of NEC in patients with CHD. Medline, Cochrane, and EMBASE were searched from 1946 through 2023 for studies of NEC in infants 0-12 months of age with CHD. Risk of bias was assessed with validated tools for incidence and risk factors. Pooled estimates were meta-analyzed by risk of bias or synthesized without meta-analysis. Eighty-six studies with a total of 67,924 participants were included. The incidence of cardiogenic NEC was 7.1% (95% CI 4.7-10.5%) in term infants and 13.0% (10.2-16.5%) in low birthweight preterm infants. NEC required surgery in 0.8% (0.5-1.1%) of term and 2.7% (2.0-3.7%) of premature infants, respectively. Only gestational age and birth weight were consistently associated with risk of NEC. Restricting pooled estimates to studies of moderate or low risk of bias significantly reduced the number of studies included. Necrotizing enterocolitis is a common cause of morbidity in infants with CHD, but additional research is needed to determine which infants are at highest risk of developing NEC and would benefit most from a change in management. This systematic review and meta-analysis was conducted according to a prespecified protocol registered at the Prospective Register of Systematic Reviews (CRD42021282114).

Mitochondrial Dysfunction in Heart Failure: From Pathophysiological Mechanisms to Therapeutic Opportunities.

Mitochondrial dysfunction, a feature of heart failure, leads to a progressive decline in bioenergetic reserve capacity, consisting in a shift of energy production from mitochondrial fatty acid oxidation to glycolytic pathways. This adaptive process of cardiomyocytes does not represent an effective strategy to increase the energy supply and to restore the energy homeostasis in heart failure, thus contributing to a vicious circle and to disease progression. The increased oxidative stress causes cardiomyocyte apoptosis, dysregulation of calcium homeostasis, damage of proteins and lipids, leakage of mitochondrial DNA, and inflammatory responses, finally stimulating different signaling pathways which lead to cardiac remodeling and failure. Furthermore, the parallel neurohormonal dysregulation with angiotensin II, endothelin-1, and sympatho-adrenergic overactivation, which occurs in heart failure, stimulates ventricular cardiomyocyte hypertrophy and aggravates the cellular damage. In this review, we will discuss the pathophysiological mechanisms related to mitochondrial dysfunction, which are mainly dependent on increased oxidative stress and perturbation of the dynamics of membrane potential and are associated with heart failure development and progression. We will also provide an overview of the potential implication of mitochondria as an attractive therapeutic target in the management and recovery process in heart failure.

The Study of Echocardiography of Left Ventricle Segmentation Combining Transformer and Convolutional Neural Networks.

Accurate prediction of echocardiographic parameters is essential for diagnosis and treatment of cardiac disease, especially for segmentation of the left ventricle to obtain measurements such as left ventricular ejection fraction and volume. However, manually outlining left ventricle on echocardiographic images is a time-consuming and physician experience-dependent task. Therefore, it is crucial to develop an accurate and efficient automatic segmentation tool. Therefore, we aimed to explore a model to perform echocardiography of left ventricle segmentation by combining transformer and convolutional neural networks (CNN).ResNet-50 was used in CNN branch. The encoder-decoder architecture was used for transformer branch, which was fused to the corresponding feature maps of the CNN branches. Fusion module was used to effectively combine feature information from the CNN and transformer. Bridge attention used to increase sensitivity and prediction accuracy of model. The entire network was trained end-to-end using the binary cross-entropy with logits loss L.In this work, we propose an automatic left ventricular (LV) segmentation model based on Transformer and CNN that efficiently captures global dependencies and spatial details and create a fusion module using CBAM that fuses Transformer and CNN features. In addition, attention is also computed using multi-level fusion features to obtain the final attention segmentation map. The model was trained and evaluated on a large cardiac image dataset, EchoNet-Dynamic, with test dice coefficient of 92.4%.The results show that our model can better segment left ventricle. We also tested our model on clinical patient ultrasound images, and visualization results proved effectiveness of the model.

Nurses' Experiences With Virtual Consultations and Home-Monitoring in Patients With Cardiac Disease: A Systematic Review and Qualitative Meta-Synthesis of Results.

To explore how nurses experience facilitators and barriers to the use of video-consultations for home-monitoring of patients with cardiac disease. A systematic literature search in PubMed, CINAHL, Scopus, and Web of Science was undertaken, inclusion criteria were qualitative data published between 2013 and 2023 written in English, Norwegian, Swedish, or Danish. Ten studies were included in the qualitative synthesis conducted as described by Braun and Clarke. From the synthesis, a main theme emerged: Nurses' uncertainty toward telemedicine is a risk toward the use of video-consultations and home-monitoring. The essence of the findings range from nurses' positive experiences to their frustration concerning the implementation process and the lack of technical support for clinicians and patients. Nurses often felt frustration and uncertainty about the quality of delivered care through virtual consultations. Working with technology in caring for patients with cardiac disease, including video-consultations and home-monitoring, nurses experienced a sense of insecurity. Insecurity was identified as a lack of technological knowledge, nurses' feelings of apathy, poorer observation through a video-consultation, and the lack of organizational support.

Bone marrow contribution to the heart from development to adulthood.

Cardiac chamber walls contain large numbers of non-contractile interstitial cells, including fibroblasts, endothelial cells, pericytes and significant populations of blood lineage-derived cells. Blood cells first colonize heart tissues a few days before birth, although their recruitment from the bloodstream to the cardiac interstitium is continuous and extends throughout adult life. The bone marrow, as the major hematopoietic site of adult individuals, is in charge of renewing all circulating cell types, and it therefore plays a pivotal role in the incorporation of blood cells to the heart. Bone marrow-derived cells are instrumental to tissue homeostasis in the steady-state heart, and are major effectors in cardiac disease progression. This review will provide a comprehensive approach to bone marrow-derived blood cell functions in the heart, and discuss aspects related to hot topics in the cardiovascular field like cell-based heart regeneration strategies.

Clear and present danger: Mechanical heart valves in pregnancy.

Specialists in Obstetric Haematology continue to be challenged by pregnant women with mechanical heart valves, who are at high risk of death or severe morbidity. Effective anticoagulation to reduce valve thrombosis inevitably increases risk of obstetric haemorrhage and fetal loss or harm, and difficult decisions need to be made. Lester and mulitdisciplinary colleagues on behalf of the British Society for Haematology review available evidence and provide comprehensive recommendations to guide management in this difficult area. Commentary on: Lester et al. British Society for Haematology guideline for anticoagulant management of pregnant individuals with mechanical heart valves. Br J Haematol 2023;202:465-478.