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Induction of reactive oxygen species (ROS) production in cancer cells plays a critical role for cancer treatment. However, therapeutic efficiency remains challenging due to insufficient ROS production of current ROS inducers. We designed a novel platinum (Pt)-based drug named "carrier-platin" that integrates ultrasmall Pt-based nanoparticles uniformly confined within a poly(amino acids) carrier. Carrier-platin dramatically triggered a burst of ROS in cancer cells, leading to cancer cell death as quick as 30 min. Unlike traditional Pt-based drugs which induce cell apoptosis through DNA intercalation, carrier-platin with superior ROS catalytic activities induces a unique pattern of cancer cell death that is neither apoptosis nor ferroptosis and operates independently of DNA damage. Importantly, carrier-platin demonstrates superior anti-tumor efficacy against a broad spectrum of cancers, particularly those with multidrug resistance, while maintaining minimal systemic toxicity. Our findings reveal a distinct mechanism of action of Pt in cancer cell eradication, positioning carrier-platin as a novel category of anti-cancer chemotherapeutics.

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Intracardiac hemodynamics plays a crucial role in the onset and development of cardiac and valvular diseases. Simulations of blood flow in the left ventricle (LV) have provided valuable insight into assessing LV hemodynamics. While fully coupled fluid-solid modelings of the LV remain challenging due to the complex passive-active behavior of the LV wall myocardium, the integration of imaging-driven quantification of structural motion with computational fluid dynamics (CFD) modeling in the LV holds the promise of feasible and clinically translatable characterization of patient-specific LV hemodynamics. In this study, we propose to integrate two magnetic resonance imaging (MRI) modalities with the moving-boundary CFD method to characterize intracardiac LV hemodynamics. Our method uses the standard cine cardiac magnetic resonance (CMR) images to estimate four-dimensional myocardial motion, eliminating the need for involved myocardial material modeling to capture LV wall behavior. In conjunction with CMR, phase contrast-MRI (PC-MRI) was used to measure temporal blood inflow rates at the mitral orifice, serving as an additional boundary condition. Flow patterns, including velocity streamlines, vortex rings, and kinetic energy, were characterized and compared to the available data. Moreover, relationships between LV wall kinematic markers and flow characteristics were determined without myocardial material modeling and using a non-rigid image registration (NRIR) method. The fidelity of the simulation was quantitatively evaluated by validating the flow rate at the aortic outflow tract against respective PC-MRI measures. The proposed methodology offers a novel and feasible toolset that works with standard PC-CMR protocols to improve the clinical assessment of LV characteristics in prognostic studies and surgical planning.

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The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

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The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

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Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

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The development of external stimuli-controlled payload systems has been sought after with increasing interest toward magnetothermally-triggered drug release (MTDR) carriers due to their non-invasive features. However, current MTDR carriers present several limitations, such as poor heating efficiency caused by the aggregation of iron oxide nanoparticles (IONPs) or the presence of antiferromagnetic phases which affect their efficiency. Herein, a novel MTDR carrier is developed using a controlled encapsulation method that fully fixes and confines IONPs of various sizes within the metal-organic frameworks (MOFs). This novel carrier preserves the MOF's morphology, porosity, and IONP segregation, while enhances heating efficiency through the oxidation of antiferromagnetic phases in IONPs during encapsulation. It also features a magnetothermally-responsive nanobrush that is stimulated by an alternating magnetic field to enable on-demand drug release. The novel carrier shows improved heating, which has potential applications as contrast agents and for combined chemo and magnetic hyperthermia therapy. It holds a great promise for magneto-thermally modulated drug dosing at tumor sites, making it an exciting avenue for cancer treatment.

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The ability of endothelial cells to sense and respond to dynamic changes in blood flow is critical for vascular homeostasis and cardiovascular health. The mechanical and geometric properties of the nuclear and cytoplasmic compartments affect mechanotransduction. We hypothesized that alterations to these parameters have resulting mechanosensory consequences. Using atomic force microscopy and mathematical modeling, we assessed how the nuclear and cytoplasmic compartment stiffnesses modulate shear stress transfer to the nucleus within aging endothelial cells. Our computational studies revealed that the critical parameter controlling shear transfer is not the individual mechanics of these compartments, but the stiffness ratio between them. Replicatively aged cells had a reduced stiffness ratio, attenuating shear transfer, while the ratio was not altered in a genetic model of accelerated aging. We provide a theoretical framework suggesting that dysregulation of the shear stress response can be uniquely imparted by relative mechanical changes in subcellular compartments.

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The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

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Smart rings provide unique opportunities for continuous physiological measurement. They are easy to wear, provide little burden in comparison to other smart wearables, are suitable for nocturnal settings, and can be sized to provide ideal contact between the sensors and the skin at all times. Continuous measuring of blood pressure (BP) provides essential diagnostic and prognostic value for cardiovascular health management. However, conventional ambulatory BP measurement devices operate using an inflating cuff that is bulky, intrusive, and impractical for frequent or continuous measurements. We introduce ring-shaped bioimpedance sensors leveraging the deep tissue sensing ability of bioimpedance while introducing no sensitivity to skin tones, unlike optical modalities. We integrate unique human finger finite element model with exhaustive experimental data from participants and derive optimum design parameters for electrode placement and sizes that yields highest sensitivity to arterial volumetric changes, with no discrimination against varying skin tones. BP is constructed using machine learning algorithms. The ring sensors are used to estimate arterial BP showing peak correlations of 0.81, and low error (systolic BP: 0.11 ± 5.27 mmHg, diastolic BP: 0.11 ± 3.87 mmHg) for >2000 data points and wide BP ranges (systolic: 89-213 mmHg and diastolic: 42-122 mmHg), highlighting the significant potential use of bioimpedance ring for accurate and continuous estimation of BP.

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The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model the input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need to be trained with a significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would reduce reliance on ground truth information. We achieve this by building Taylor's approximation for the gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series regression models tested on the same datasets, we retain a high correlation (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.

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The demand for non-obtrusive, accurate, and continuous blood pressure (BP) monitoring systems is becoming more prevalent with the realization of its significance in preventable cardiovascular disease (CVD) globally. Current cuff-based standards are bulky, uncomfortable, and are limited to discrete recording periods. Wearable sensor technologies such as those using optical photoplethysmography (PPG) have been used to develop blood pressure estimation models through a variety of methods. However, this technology falls short as optical based systems have bias favoring lighter skin tones and lower body fat compositions. Bioimpedance (Bio-Z) is a capable modality of sensing arterial blood flow without implicit inadvertent bias towards individuals. In this paper we propose a ring-based bioimpedance system to capture arterial blood flow from the digital artery of the finger. The ring design provides a more compact wearable device utilizing only a single Bio-Z channel, making it a familiar fit to individuals. Post-processing the acquired Bio-Z signals, we extracted 9 frequency domain features from windowed beat cycles to train subject specific regression models. Results indicate the average mean absolute errors for systolic/diastolic BP to be 4.38/3.63mmHg, consistent with AAMI standards.

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OBJECTIVE: Local Liquid drug (LLD) delivery devices have recently emerged as a novel approach to treat peripheral arterial disease. This systemic review aims to identify and evaluate the clinical utility of the most commonly used delivery devices. METHODS: A systemic review was performed using the Medical Subjects Heading terms of "drug delivery," "liquid," "local," and "cardiovascular disease" in PubMed, Google Scholar, and Scopus. RESULTS: Four commonly used delivery devices were identified, including (1) the Bullfrog Micro-Infusion Device, (2) the ClearWay RX Catheter, (3) the Occlusion Perfusion Catheter, and (4) the Targeted Adjustable Pharmaceutical Administration. All have shown to successfully deliver liquid therapeutic into the target lesion and have exhibited favorable safety and efficacy profiles in preclinical and clinical trials. The LLD devices have the ability to treat very long or multiple lesions with a single device, providing a more economical option. The safety profile in LLD clinical studies is also favorable in view of recent concerns regarding adverse events with crystalline-paclitaxel-coated devices. CONCLUSION: There is clear clinical evidence to support the concept of local liquid delivery to treat occlusive arterial disease. CLINICAL IMPACT: The 'leave nothing behind' strategy has been at the forefront of the most recent innovations in the field of interventional cardiology and vascular interventions. Although drug coated balloons have overcome limitations associated with plain old balloon angioplasty and peripheral stents, recent safety concerns and cost considerations have impacted their usage. In this review, various liquid drug delivery devices are presented, showcasing their capabilities and success in both preclinical and clinical settings. These innovative liquid delivery devices, capable of targeted delivery and their ability to be re-used for multiple treatment sites, may provide solutions for current unmet clinical needs.

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There has been a sea change in the scientific world, advanced even more rapidly by the recent compounded public crises. Accelerated discovery, and impact from such discoveries have come from convergence approaches across disciplines, sectors, institutions, and the multiple communities seeking the common goal of innovations that transform. The classic simultaneous pursuit of fundamental understanding and application has been termed  Pasteur's quadrant, where use-inspired basic research occurs. In the classic schematic developed by Donald Stokes, three quadrants  represent research approaches using a 2D plane in which the vertical dimension represents the quest for understanding (basic research) and the horizontal dimension represents the consideration of use (applied research). The three outer quadrants are Bohr's (pure basic research), Edison's (pure applied research), and Pasteur's (use-inspired  basic research). Viewing each of these axes as a continuum, we label the previously unnamed but contributory cell as the Innominate quadrant, where a nonzero amount of discovery and applied research also has value in generating scientific tools, novel processes or products that inform the other quadrants. More importantly, a reimagined Pasteur's quadrant schema shows a third dimension of Transformations over Time, occurring through a continuous fluid interchange among the quadrants.  Transformative innovations may originate from any single quadrant.  While work in Pasteur's quadrant has been shown to be highly productive, a dynamic fluid interchange among the quadrants is often involved and generates transformative advances at a faster rate. This should inform how we fund science, engineeering, and medicine and educate the next generation of innovators.

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Despite the advance of immunotherapy, only a small subset of patients gains long-term survival benefit. This fact represents a compelling rationale to develop immuno-PET imaging that can predict tumor response to immunotherapy. An increasing number of studies have shown that tumor-specific major histocompatibility complex II (tsMHC-II) is associated with improved responses to targeted immunotherapy. The aim of this study was to investigate the potential of tsMHC-II protein expression and its dynamic change on treatment with interferon γ (IFNγ) as a new target for immuno-PET to predict response to immunotherapy. Methods: Major histocompatibility complex II (MHC-II) antibody was radiolabeled with DOTA-chelated 64Cu to derive an MHC-II immuno-PET tracer. Two melanoma models (B16SIY, B16F10) that are respondent and nonrespondent, respectively, to PD1/PD-L1 checkpoint inhibitor were used. Both tumor models were treated with anti-PD1 and IFNγ, enabling observation of dynamic changes in tsMHC-II. Small-animal PET imaging, biodistribution, and histologic studies were performed to validate the correlation of tsMHC-II with the tumor response to the immunotherapy. Results: Fluorescence-activated cell sorting analysis of the 2 tumors supported the consensual recognition of tsMHC-II correlated with the tumor response to the immunotherapy. The in vivo PET imaging revealed higher basal levels of tsMHC-II in the responder, B16SIY, than in the nonresponder, B16F10. When treated with anti-PD1 antibody in animals, B16SIY tumors displayed a sensitive increase in tsMHC-II compared with B16F10 tumors. In IFNγ stimulation groups, the greater magnitude of tsMHC-II was further amplified when the IFNγ signaling was activated in the B16SIY tumors, as IFNγ signaling positively upregulates tsMHC-II in the tumor immunity. Subsequent histopathologic analysis supported the correlative characteristics of tsMHC-II with tumor immunity and response to cancer immunotherapy. Conclusion: Collectively, the predictive value of tsMHC-II immuno-PET was validated for stratifying tumor immunotherapy responders versus nonresponders. Monitoring sensitivity of tsMHC-II to IFNγ stimulation may provide an effective strategy to predict the tumor response to immunotherapy.

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Perfusion catheters have recently emerged as a novel approach to deliver liquid anti-proliferative agents into flow obstructed arterial segments. The purpose of this study was to determine the impact of luminal delivery pressure on liquid drug penetration into the vessel wall. An ex vivo model using harvested porcine carotid arteries and a two-dimensional computational model were utilized to determine the impact of delivery pressure of liquid therapy into the arterial wall. A pig peripheral injury model determined the impact of intra-luminal delivery pressure on drug retention. Ex vivo results demonstrated that depth of fluid penetration varies from 6.93 ± 1.90% at 0 atm to 27.75 ± 6.61% penetration of the medial layer at 0.4 atm. Computational results had similar outcomes, as penetration varied between 4.4% and 22.84%. The in vivo results demonstrated significant increase in drug delivery to the arterial tissue at 0.4 atm versus 0.1 atm at 1 h (23.43 ± 13.59 ng/mg vs. 2.49 ± 1.81 ng/mg, p = 0.026) and 7 days (0.50 ± 0.39 ng/mg vs. 0.018 ± 0.023 ng/mg, p = 0.0496). The result of this study provides an innovative strategic and technical approach to enable targeted liquid therapy.

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Recent advances in the generation, purification and cellular delivery of RNA have enabled development of RNA-based therapeutics for a broad array of applications. RNA therapeutics comprise a rapidly expanding category of drugs that will change the standard of care for many diseases and actualize personalized medicine. These drugs are cost effective, relatively simple to manufacture, and can target previously undruggable pathways. It is a disruptive therapeutic technology, as small biotech startups, as well as academic groups, can rapidly develop new and personalized RNA constructs. In this review we discuss general concepts of different classes of RNA-based therapeutics, including antisense oligonucleotides, aptamers, small interfering RNAs, microRNAs, and messenger RNA. Furthermore, we provide an overview of the RNA-based therapies that are currently being evaluated in clinical trials or have already received regulatory approval. The challenges and advantages associated with use of RNA-based drugs are also discussed along with various approaches for RNA delivery. In addition, we introduce a new concept of hospital-based RNA therapeutics and share our experience with establishing such a platform at Houston Methodist Hospital.

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With continued advances in science and technology, there is great potential to extend our healthspan as we age.

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