Cracking the code of the cardiovascular enigma: hPSC-derived endothelial cells unveil the secrets of endothelial dysfunction.

Endothelial dysfunction is a central contributor to the development of most cardiovascular diseases and is characterised by the reduced synthesis or bioavailability of the vasodilator nitric oxide together with other abnormalities such as inflammation, senescence, and oxidative stress. The use of patient-specific and genome-edited human pluripotent stem cell-derived endothelial cells (hPSC-ECs) has shed novel insights into the role of endothelial dysfunction in cardiovascular diseases with strong genetic components such as genetic cardiomyopathies and pulmonary arterial hypertension. However, their utility in studying complex multifactorial diseases such as atherosclerosis, metabolic syndrome and heart failure poses notable challenges. In this review, we provide an overview of the different methods used to generate and characterise hPSC-ECs before comprehensively assessing their effectiveness in cardiovascular disease modelling and high-throughput drug screening. Furthermore, we explore current obstacles that will need to be overcome to unleash the full potential of hPSC-ECs in facilitating patient-specific precision medicine. Addressing these challenges holds great promise in advancing our understanding of intricate cardiovascular diseases and in tailoring personalised therapeutic strategies.

The new era of cardiovascular research: revolutionizing cardiovascular research with 3D models in a dish.

Cardiovascular research has heavily relied on studies using patient samples and animal models. However, patient studies often miss the data from the crucial early stage of cardiovascular diseases, as obtaining primary tissues at this stage is impracticable. Transgenic animal models can offer some insights into disease mechanisms, although they usually do not fully recapitulate the phenotype of cardiovascular diseases and their progression. In recent years, a promising breakthrough has emerged in the form of in vitro three-dimensional (3D) cardiovascular models utilizing human pluripotent stem cells. These innovative models recreate the intricate 3D structure of the human heart and vessels within a controlled environment. This advancement is pivotal as it addresses the existing gaps in cardiovascular research, allowing scientists to study different stages of cardiovascular diseases and specific drug responses using human-origin models. In this review, we first outline various approaches employed to generate these models. We then comprehensively discuss their applications in studying cardiovascular diseases by providing insights into molecular and cellular changes associated with cardiovascular conditions. Moreover, we highlight the potential of these 3D models serving as a platform for drug testing to assess drug efficacy and safety. Despite their immense potential, challenges persist, particularly in maintaining the complex structure of 3D heart and vessel models and ensuring their function is comparable to real organs. However, overcoming these challenges could revolutionize cardiovascular research. It has the potential to offer comprehensive mechanistic insights into human-specific disease processes, ultimately expediting the development of personalized therapies.

Sphingolipid metabolism controls mammalian heart regeneration.

Utilization of lipids as energy substrates after birth causes cardiomyocyte (CM) cell-cycle arrest and loss of regenerative capacity in mammalian hearts. Beyond energy provision, proper management of lipid composition is crucial for cellular and organismal health, but its role in heart regeneration remains unclear. Here, we demonstrate widespread sphingolipid metabolism remodeling in neonatal hearts after injury and find that SphK1 and SphK2, isoenzymes producing the same sphingolipid metabolite sphingosine-1-phosphate (S1P), differently regulate cardiac regeneration. SphK2 is downregulated during heart development and determines CM proliferation via nuclear S1P-dependent modulation of histone acetylation. Reactivation of SphK2 induces adult CM cell-cycle re-entry and cytokinesis, thereby enhancing regeneration. Conversely, SphK1 is upregulated during development and promotes fibrosis through an S1P autocrine mechanism in cardiac fibroblasts. By fine-tuning the activity of each SphK isoform, we develop a therapy that simultaneously promotes myocardial repair and restricts fibrotic scarring to regenerate the infarcted adult hearts.

Artificial Intelligence in Surgery: The Future is Now.

Background Clinical Artificial intelligence (AI) has reached a critical inflection point. Advances in algorithmic science and increased understanding of operational considerations in AI deployment are opening the door to widespread clinical pathway transformation. For surgery in particular, the application of machine learning algorithms in fields such as computer vision and operative robotics are poised to radically change how we screen, diagnose, risk-stratify, treat and follow-up patients, in both pre- and post-operative stages, and within operating theatres. Summary In this paper, we summarise the current landscape of existing and emerging integrations within complex surgical care pathways. We investigate effective methods for practical use of AI throughout the patient pathway, from early screening and accurate diagnosis to intraoperative robotics, post-operative monitoring and follow-up. Horizon scanning of AI technologies in surgery is used to identify novel innovations that can enhance surgical practice today, with potential for paradigm shifts across core domains of surgical practice in the future. Any AI-driven future must be built on responsible and ethical usage, reinforced by effective oversight of data governance, and of risks to patient safety in deployment. Implementation is additionally bound to considerations of usability and pathway feasibility, and the need for robust healthcare technology assessment and evidence generation. While these factors are traditionally seen as barriers to translating AI into practice, we discuss how holistic implementation practices can create a solid foundation for scaling AI across pathways. Key Messages The next decade will see rapid translation of experimental development into real-world impact. AI will require evolution of work practices, but will also enhance patient safety, enhance surgical quality outcomes, and provide significant value for surgeons and health systems. Surgical practice has always sat on a bedrock of technological innovation. For those that follow this tradition, the future of AI in surgery starts now.

Quantifying digital health inequality across a national healthcare system.

OBJECTIVES: Digital health inequality, observed as differential utilisation of digital tools between population groups, has not previously been quantified in the National Health Service (NHS). Deployment of universal digital health interventions, including a national smartphone app and online primary care services, allows measurement of digital inequality across a nation. We aimed to measure population factors associated with digital utilisation across 6356 primary care providers serving the population of England. METHODS: We used multivariable regression to test association of population and provider characteristics (including patient demographics, socioeconomic deprivation, disease burden, prescribing burden, geography and healthcare provider resource) with activation of two independent digital services during 2021/2022. RESULTS: We find a significant adjusted association between increased population deprivation and reduced digital utilisation across both interventions. Multivariable regression coefficients for most deprived quintiles correspond to 4.27 million patients across England where deprivation is associated with non-activation of the NHS App. CONCLUSION: Results are concerning for technologically driven widening of healthcare inequalities. Targeted incentive to digital is necessary to prevent digital disparity from becoming health outcomes disparity.

Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation.

The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.

A new tool for evaluating health equity in academic journals; the Diversity Factor.

Current methods to evaluate a journal's impact rely on the downstream citation mapping used to generate the Impact Factor. This approach is a fragile metric prone to being skewed by outlier values and does not speak to a researcher's contribution to furthering health outcomes for all populations. Therefore, we propose the implementation of a Diversity Factor to fulfill this need and supplement the current metrics. It is composed of four key elements: dataset properties, author country, author gender and departmental affiliation. Due to the significance of each individual element, they should be assessed independently of each other as opposed to being combined into a simplified score to be optimized. Herein, we discuss the necessity of such metrics, provide a framework to build upon, evaluate the current landscape through the lens of each key element and publish the findings on a freely available website that enables further evaluation. The OpenAlex database was used to extract the metadata of all papers published from 2000 until August 2022, and Natural language processing was used to identify individual elements. Features were then displayed individually on a static dashboard developed using TableauPublic, which is available at www.equitablescience.com. In total, 130,721 papers were identified from 7,462 journals where significant underrepresentation of LMIC and Female authors was demonstrated. These findings are pervasive and show no positive correlation with the Journal's Impact Factor. The systematic collection of the Diversity Factor concept would allow for more detailed analysis, highlight gaps in knowledge, and reflect confidence in the translation of related research. Conversion of this metric to an active pipeline would account for the fact that how we define those most at risk will change over time and quantify responses to particular initiatives. Therefore, continuous measurement of outcomes across groups and those investigating those outcomes will never lose importance. Moving forward, we encourage further revision and improvement by diverse author groups in order to better refine this concept.

Recent advances in regulating the proliferation or maturation of human-induced pluripotent stem cell-derived cardiomyocytes.

In the last decade, human-induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM)-based cell therapy has drawn broad attention as a potential therapy for treating injured hearts. However, mass production of hiPSC-CMs remains challenging, limiting their translational potential in regenerative medicine. Therefore, multiple strategies including cell cycle regulators, small molecules, co-culture systems, and epigenetic modifiers have been used to improve the proliferation of hiPSC-CMs. On the other hand, the immaturity of these proliferative hiPSC-CMs could lead to lethal arrhythmias due to their limited ability to functionally couple with resident cardiomyocytes. To achieve functional maturity, numerous methods such as prolonged culture, biochemical or biophysical stimulation, in vivo transplantation, and 3D culture approaches have been employed. In this review, we summarize recent approaches used to promote hiPSC-CM proliferation, and thoroughly review recent advances in promoting hiPSC-CM maturation, which will serve as the foundation for large-scale production of mature hiPSC-CMs for future clinical applications.

Impact of primary to secondary care data sharing on care quality in NHS England hospitals.

Health information exchange (HIE) is seen as a key component of effective care but remains poorly evidenced at a health system level. In the UK National Health Service (NHS), the ability to share primary care data with secondary care clinicians is a focus of continued digital investment. In this study, we report the evolution of interoperable technology across a period of rapid digital transformation in NHS England from 2015 to 2019, and test association of primary to secondary care data-sharing capabilities with clinical care quality indicators across all acute secondary care providers (n = 135 NHS Trusts). In multivariable analyses, data-sharing capabilities are associated with reduction in patients breaching an Accident & Emergency (A&E) 4-h decision time threshold, and better patient-reported experience of acute hospital care quality. Using synthetic control analyses, we estimate mean 2.271% (STD+/-3.371) absolute reduction in A&E 4-h decision time breach, 12 months following introduction of data-sharing capabilities. Our findings support current digital transformation programmes for developing regional HIE networks but highlight the need to focus on implementation factors in addition to technological procurement.

Case Report of Improvement in Long-COVID Symptoms in an Air Force Medic Treated With Transcranial Magnetic Stimulation Using Electro-Magnetic Brain Pulse Technique.

Long-coronavirus disease (COVID) is an ill-defined set of symptoms persisting in patients following infection with COVID-19 that range from any combination of persistent breathing difficulties to anosmia, impaired attention, memory, fatigue, or pain. Recently, noninvasive transcutaneous electrical brain stimulation techniques have been showing early signs of success in addressing some of these complaints. We postulate that the use of a stimulation technique with transcranial magnetic stimulation may also similarly be effective. A 36-year-old male suffering from symptoms of dyspnea, anosmia, and "brain fog" for 2 years following coronavirus infection was treated with 10 sessions of Electro-Magnetic Brain Pulse (EMBP®), a personalized transcranial magnetic stimulation protocol guided by the patient's electroencephalograph (EEG). At the conclusion of the treatment, the patient had improvements in mood, sense of smell, and brain fogging. Dyspnea also decreased with a gain of 11% forced expiratory volume 1/forced vital capacity. A high-sensitivity athletic training cognitive test showed an overall 27% increase in aggregate score. A significant portion of this was attributed to changes in visual clarity and decision-making speed. Post-treatment EEG showed a shift from predominantly delta waves to more synchronized alpha wave patterns during the resting state. Brain stimulation techniques appear to be showing early signs of success with long-COVID symptoms. This is the first case describing the use of a magnetic stimulation technique with quantitative test results and recorded EEG changes. Given the early success in this patient with cognition, dyspnea, and anosmia, this noninvasive treatment modality warrants further research.

Encapsulated papillary carcinoma of the breast: A case report.

Breast malignancies diagnosed from screening mammography most commonly present as a new or enlarging lesion at the time of diagnosis. We present the unusual case of an encapsulated papillary carcinoma that demonstrated decreasing size on mammogram over several subsequent years. We review the unique imaging and histologic findings of papillary carcinoma that make this imaging conundrum possible.

TRF2 rescues telomere attrition and prolongs cell survival in Duchenne muscular dystrophy cardiomyocytes derived from human iPSCs.

Duchenne muscular dystrophy (DMD) is a severe muscle wasting disease caused by the lack of dystrophin. Heart failure, driven by cardiomyocyte death, fibrosis, and the development of dilated cardiomyopathy, is the leading cause of death in DMD patients. Current treatments decrease the mechanical load on the heart but do not address the root cause of dilated cardiomyopathy: cardiomyocyte death. Previously, we showed that telomere shortening is a hallmark of DMD cardiomyocytes. Here, we test whether prevention of telomere attrition is possible in cardiomyocytes differentiated from patient-derived induced pluripotent stem cells (iPSC-CMs) and if preventing telomere shortening impacts cardiomyocyte function. We observe reduced cell size, nuclear size, and sarcomere density in DMD iPSC-CMs compared with healthy isogenic controls. We find that expression of just one telomere-binding protein, telomeric repeat-binding factor 2 (TRF2), a core component of the shelterin complex, prevents telomere attrition and rescues deficiencies in cell size as well as sarcomere density. We employ a bioengineered platform to micropattern cardiomyocytes for calcium imaging and perform Southern blots of telomere restriction fragments, the gold standard for telomere length assessments. Importantly, preservation of telomere lengths in DMD cardiomyocytes improves their viability. These data provide evidence that preventing telomere attrition ameliorates deficits in cell morphology, activation of the DNA damage response, and premature cell death, suggesting that TRF2 is a key player in DMD-associated cardiac failure.

Impaired Human Cardiac Cell Development due to NOTCH1 Deficiency.

BACKGROUND: NOTCH1 pathogenic variants are implicated in multiple types of congenital heart defects including hypoplastic left heart syndrome, where the left ventricle is underdeveloped. It is unknown how NOTCH1 regulates human cardiac cell lineage determination and cardiomyocyte proliferation. In addition, mechanisms by which NOTCH1 pathogenic variants lead to ventricular hypoplasia in hypoplastic left heart syndrome remain elusive. METHODS: CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 genome editing was utilized to delete NOTCH1 in human induced pluripotent stem cells. Cardiac differentiation was carried out by sequential modulation of WNT signaling, and NOTCH1 knockout and wild-type differentiating cells were collected at day 0, 2, 5, 10, 14, and 30 for single-cell RNA-seq. RESULTS: Human NOTCH1 knockout induced pluripotent stem cells are able to generate functional cardiomyocytes and endothelial cells, suggesting that NOTCH1 is not required for mesoderm differentiation and cardiovascular development in vitro. However, disruption of NOTCH1 blocks human ventricular-like cardiomyocyte differentiation but promotes atrial-like cardiomyocyte generation through shortening the action potential duration. NOTCH1 deficiency leads to defective proliferation of early human cardiomyocytes, and transcriptomic analysis indicates that pathways involved in cell cycle progression and mitosis are downregulated in NOTCH1 knockout cardiomyocytes. Single-cell transcriptomic analysis reveals abnormal cell lineage determination of cardiac mesoderm, which is manifested by the biased differentiation toward epicardial and second heart field progenitors at the expense of first heart field progenitors in NOTCH1 knockout cell populations. CONCLUSIONS: NOTCH1 is essential for human ventricular-like cardiomyocyte differentiation and proliferation through balancing cell fate determination of cardiac mesoderm and modulating cell cycle progression. Because first heart field progenitors primarily contribute to the left ventricle, we speculate that pathogenic NOTCH1 variants lead to biased differentiation of first heart field progenitors, blocked ventricular-like cardiomyocyte differentiation, and defective cardiomyocyte proliferation, which collaboratively contribute to left ventricular hypoplasia in hypoplastic left heart syndrome.

Moving towards vertically integrated artificial intelligence development.

Substantial interest and investment in clinical artificial intelligence (AI) research has not resulted in widespread translation to deployed AI solutions. Current attention has focused on bias and explainability in AI algorithm development, external validity and model generalisability, and lack of equity and representation in existing data. While of great importance, these considerations also reflect a model-centric approach seen in published clinical AI research, which focuses on optimising architecture and performance of an AI model on best available datasets. However, even robustly built models using state-of-the-art algorithms may fail once tested in realistic environments due to unpredictability of real-world conditions, out-of-dataset scenarios, characteristics of deployment infrastructure, and lack of added value to clinical workflows relative to cost and potential clinical risks. In this perspective, we define a vertically integrated approach to AI development that incorporates early, cross-disciplinary, consideration of impact evaluation, data lifecycles, and AI production, and explore its implementation in two contrasting AI development pipelines: a scalable "AI factory" (Mayo Clinic, Rochester, United States), and an end-to-end cervical cancer screening platform for resource poor settings (Paps AI, Mbarara, Uganda). We provide practical recommendations for implementers, and discuss future challenges and novel approaches (including a decentralised federated architecture being developed in the NHS (AI4VBH, London, UK)). Growth in global clinical AI research continues unabated, and introduction of vertically integrated teams and development practices can increase the translational potential of future clinical AI projects.