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  1. Cardiac tissue engineering is an emerging field providing tools to treat and study cardiovascular diseases (CVDs). In the past years, the integration of stem cell technologies with micro- and nanoengineering techniques has enabled the creation of novel engineered cardiac tissues (ECTs) with potential applications in disease modeling, drug screening, and regenerative medicine. However, a major unaddressed limitation of stem cell-derived ECTs is their immature state, resembling a neonatal phenotype and genotype. The modulation of the cellular microenvironment within the ECTs has been proposed as an efficient mechanism to promote cellular maturation and improve features such as cellular coupling and synchronization. The integration of biological and nanoscale cues in the ECTs could serve as a tool for the modification and control of the engineered tissue microenvironment. Here we present a proof-of-concept study for the integration of biofunctionalized gold nanoribbons (AuNRs) with hiPSC-derived isogenic cardiac organoids to enhance tissue function and maturation. We first present extensive characterization of the synthesized AuNRs, their PEGylation and cytotoxicity evaluation. We then evaluated the functional contractility and transcriptomic profile of cardiac organoids fabricated with hiPSC-derived cardiomyocytes (mono-culture) as well as with hiPSC-derived cardiomyocytes and cardiac fibroblasts (co-culture). We demonstrated that PEGylated AuNRs are biocompatible and do not induce cell death in hiPSC-derived cardiac cells and organoids. We also found an improved transcriptomic profile of the co-cultured organoids indicating maturation of the hiPSC-derived cardiomyocytes in the presence of cardiac fibroblasts. Overall, we present for the first time the integration of AuNRs into cardiac organoids, showing promising results for improved tissue function. 
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    Free, publicly-accessible full text available June 5, 2024
  2. An organotypic heart on-a-chip modeling long QT syndrome type 2 was created to study effect of R531W mutation in LQTS2 pathology. 
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  3. Abstract INTRODUCTION

    Identifying mild cognitive impairment (MCI) patients at risk for dementia could facilitate early interventions. Using electronic health records (EHRs), we developed a model to predict MCI to all‐cause dementia (ACD) conversion at 5 years.


    Cox proportional hazards model was used to identify predictors of ACD conversion from EHR data in veterans with MCI. Model performance (area under the receiver operating characteristic curve [AUC] and Brier score) was evaluated on a held‐out data subset.


    Of 59,782 MCI patients, 15,420 (25.8%) converted to ACD. The model had good discriminative performance (AUC 0.73 [95% confidence interval (CI) 0.72–0.74]), and calibration (Brier score 0.18 [95% CI 0.17–0.18]). Age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors, while body mass index, alcohol abuse, and sleep apnea were protective factors.


    EHR‐based prediction model had good performance in identifying 5‐year MCI to ACD conversion and has potential to assist triaging of at‐risk patients.


    Of 59,782 veterans with mild cognitive impairment (MCI), 15,420 (25.8%) converted to all‐cause dementia within 5 years.

    Electronic health record prediction models demonstrated good performance (area under the receiver operating characteristic curve 0.73; Brier 0.18).

    Age and vascular‐related morbidities were predictors of dementia conversion.

    Synthetic data was comparable to real data in modeling MCI to dementia conversion.

    Key Points

    An electronic health record–based model using demographic and co‐morbidity data had good performance in identifying veterans who convert from mild cognitive impairment (MCI) to all‐cause dementia (ACD) within 5 years.

    Increased age, stroke, cerebrovascular disease, myocardial infarction, hypertension, and diabetes were risk factors for 5‐year conversion from MCI to ACD.

    High body mass index, alcohol abuse, and sleep apnea were protective factors for 5‐year conversion from MCI to ACD.

    Models using synthetic data, analogs of real patient data that retain the distribution, density, and covariance between variables of real patient data but are not attributable to any specific patient, performed just as well as models using real patient data. This could have significant implications in facilitating widely distributed computing of health‐care data with minimized patient privacy concern that could accelerate scientific discoveries.

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  4. Despite significant efforts in the study of cardiovascular diseases (CVDs), they persist as the leading cause of mortality worldwide. Considerable research into human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) has highlighted their immense potential in the development of in vitro human cardiac tissues for broad mechanistic, therapeutic, and patient-specific disease modeling studies in the pursuit of CVD research. However, the relatively immature state of hPSC-CMs remains an obstacle in enhancing clinical relevance ofengineered cardiac tissue models. In this study, we describe development of a microfluidic platform for 3D modeling of cardiac tissues, derived from both rat cells and hPSC-CMs, to better recapitulate the native myocardium through co-culture with interstitial cells (specifically cardiac fibroblasts), biomimetic collagen hydrogel encapsulation, and induction of highly anisotropic tissue architecture. The presented platform is precisely engineered through incorporation of surface topography in the form of staggered microposts to enable long-term culture and maturation of cardiac cells, resulting in formation of physiologically relevant cardiac tissues with anisotropy that mimics native myocardium. After two weeks of culture, hPSC-derived cardiac tissues exhibited well-defined sarcomeric striations, highly synchronous contractions, and upregulation of several maturation genes, including HCN1, KCNQ1, CAV1.2, CAV3.1, PLN, and RYR2. These findings demonstrate the ability of the proposed engineered platform to mature animal- as well as human stem cell-derived cardiac tissues over an extended period of culture, providing a novel microfluidic chip with the capability for cardiac disease modeling and therapeutic testing. 
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  5. Cardiovascular diseases, including myocardial infarction (MI), persist as the leading cause of mortality and morbidity worldwide. The limited regenerative capacity of the myocardium presents significant challenges specifically for the treatment of MI and, subsequently, heart failure (HF). Traditional therapeutic approaches mainly rely on limiting the induced damage or the stress on the remaining viable myocardium through pharmacological regulation of remodeling mechanisms, rather than replacement or regeneration of the injured tissue. The emerging alternative regenerative medicine-based approaches have focused on restoring the damaged myocardial tissue with newly engineered functional and bioinspired tissue units. Cardiac regenerative medicine approaches can be broadly categorized into three groups: cell-based therapies, scaffold-based cardiac tissue engineering, and scaffold-free cardiac tissue engineering. Despite significant advancements, however, the clinical translation of these approaches has been critically hindered by two key obstacles for successful structural and functional replacement of the damaged myocardium, namely: poor engraftment of engineered tissue into the damaged cardiac muscle and weak electromechanical coupling of transplanted cells with the native tissue. To that end, the integration of micro- and nanoscale technologies along with recent advancements in stem cell technologies have opened new avenues for engineering of structurally mature and highly functional scaffold-based (SB-CMTs) and scaffold-free cardiac microtissues (SF-CMTs) with enhanced cellular organization and electromechanical coupling for the treatment of MI and HF. In this review article, we will present the state-of-the-art approaches and recent advancements in the engineering of SF-CMTs for myocardial repair. 
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  6. Abstract Introduction

    Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations.


    In a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM).


    The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM).


    Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.

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