Publications by authors named "Joe Zhang"

Doxorubicin is limited in its therapeutic utility due to its life-threatening cardiovascular side effects. Here, we present an integrated drug discovery pipeline combining human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (iCMs), CRISPR interference and activation (CRISPRi/a) bidirectional pooled screens, and a small-molecule screening to identify therapeutic targets mitigating doxorubicin-induced cardiotoxicity (DIC) without compromising its oncological effects. The screens revealed several previously unreported candidate genes contributing to DIC, including carbonic anhydrase 12 (CA12).

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Article Synopsis
  • - The text discusses the growing interest in large language models (LLMs) in medical contexts, emphasizing the need to evaluate their accuracy on healthcare examinations relative to established human performance standards.
  • - A systematic review of literature up to September 2023 was conducted, analyzing the accuracy of LLMs in responding to healthcare examination questions, with a strict inclusion criterion to ensure relevant and original research.
  • - The findings indicated that LLMs had an overall medical exam accuracy of 0.61 and a USMLE accuracy of 0.51, highlighting a significant gap compared to human performance metrics in medical assessments.
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Cardiovascular diseases have emerged as one of the leading causes of human mortality, but the discovery of new drugs has been hindered by the absence of suitable in vitro platforms. In recent decades, continuously refined protocols for differentiating human induced pluripotent stem cells (hiPSCs) into hiPSC-derived cardiomyocytes (hiPSC-CMs) have significantly advanced disease modeling and drug screening; however, this has led to an increasing need to monitor the function of hiPSC-CMs. The precise regulation of action potentials (APs) and intracellular calcium (Ca) transients is critical for proper excitation-contraction coupling and cardiomyocyte function.

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Objectives: Risk stratification tools that predict healthcare utilisation are extensively integrated into primary care systems worldwide, forming a key component of anticipatory care pathways, where high-risk individuals are targeted by preventative interventions. Existing work broadly focuses on comparing model performance in retrospective cohorts with little attention paid to efficacy in reducing morbidity when deployed in different global contexts. We review the evidence supporting the use of such tools in real-world settings, from retrospective dataset performance to pathway evaluation.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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Article Synopsis
  • Breast cancer typically shows as a new or growing lesion on screening mammograms, but there are exceptions.
  • The case presented is an encapsulated papillary carcinoma that surprisingly decreased in size over several years on mammograms.
  • The study discusses the specific imaging and tissue characteristics of papillary carcinoma that allow for this rare occurrence.
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With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems.

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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.

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Background: 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 regulates human cardiac cell lineage determination and cardiomyocyte proliferation. In addition, mechanisms by which pathogenic variants lead to ventricular hypoplasia in hypoplastic left heart syndrome remain elusive.

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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.

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