Metal-Organic Framework as a New Type of Magnetothermally-Triggered On-Demand Release Carrier.

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.

Liquid Drug Delivery Approaches for the Treatment of Occlusive Arterial Disease: A Systematic Review.

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.

Single-Molecule Force Measurement Guides the Design of Multivalent Ligands with Picomolar Affinity.

Interaction of multiple entities and receptors, or multivalency is widely applied to achieve high affinity ligands for diagnostic and therapeutic purposes. However, lack of knowledge on receptor distribution in living subjects remains a challenge for rational structure design. Herein, we develop a force measurement platform to probe the distribution and separation of the cell surface vascular endothelial growth factor receptors (VEGFR) in live cells, and use this to assess the geometry of appropriate linkers for distinct multivalent binding modes. A tetravalent lead ZD-4, which was developed from an antitumor drug ZD6474 (Vandetanib) with combined hybrid binding effects, yielded a 2000-fold improvement in the binding affinity to VEGFR with IC50 value of 25 pm. We confirmed the improved affinity by the associated increase of tumor uptake in the VEGFR-targeting positron emission tomography (PET) imaging using U87 tumor xenograft mouse model.

Novel platinum therapeutics induce rapid cancer cell death through triggering intracellular ROS storm.

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.

Computational modeling of left ventricular flow using PC-CMR-derived four-dimensional wall motion.

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.

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.

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.

Early adverse physiological event detection using commercial wearables: challenges and opportunities.

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.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

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.

Pulmonary vein morphology by free-breathing whole heart magnetic resonance imaging at 3 Tesla versus breathhold multi-detector computed tomography.

PURPOSE: To compare pulmonary vein and left atrial anatomy using three-dimensional free-breathing whole-heart magnetic resonance imaging (MR) at 3 Tesla (T) and multi-detector computed tomography (MDCT). MATERIALS AND METHODS: Thirty-three subjects (19 male, age 49 ± 12 years) underwent free-breathing 3T MR and contrast-enhanced MDCT during inspiratory breath hold. Pulmonary vein parameters (ostial areas, diameters, angles) were measured. RESULTS: All pulmonary veins and anomalies were identified by 3T MR and by MDCT. The right-sided pulmonary veins were directed more posteriorly, the right superior pulmonary vein more inferiorly, and the right inferior pulmonary vein more superiorly by 3T MR when compared with MDCT. The cross-sectional area, perimeters and minimum diameters of right-sided pulmonary vein ostia were significantly larger by MR, as were the maximum diameters of right and left inferior pulmonary veins. There were no significant differences between techniques in distance to first pulmonary vein branch. CONCLUSION: Pulmonary vein measurements demonstrated significant differences in angulations and dimensions when 3T MR is compared with MDCT. These differences likely represent hemodynamic and respiratory variation during free-breathing with MR versus breath-holding with MDCT. MR imaging at 3T during free-breathing offers an alternate method to define pulmonary vein and left atrial anatomy without exposure to radiation.

Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure.

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.

Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation.

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.

Continuous cuffless blood pressure monitoring with a wearable ring bioimpedance device.

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.

Disrupted Stiffness Ratio Alters Nuclear Mechanosensing.

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.

Liver metabolite concentrations measured with 1H MR spectroscopy.

PURPOSE: To determine the feasibility of measuring choline and glycogen concentrations in normal human liver in vivo with proton (hydrogen 1 [1H]) magnetic resonance (MR) spectroscopy. MATERIALS AND METHODS: Signed consent to participate in an institutional review board-approved and HIPAA-compliant study was obtained from 46 subjects (mean age, 46 years±17 [standard deviation]; 24 women) consecutively recruited during 285 days. Navigator-gated MR images were used to select 8-mL volumes for point-resolved spectroscopy (PRESS) with a 35-msec echo time. Line widths were minimized with fast breath-hold B0 field mapping and further manual shimming. Navigator-gated spectra were recorded with and without water suppression to determine metabolite concentrations with water signals as an internal reference. In three subjects, echo time was varied to determine the glycogen and choline T2. Linear regression analysis was used to examine relations between choline, hepatic lipid content, body mass index, glycogen content, and age. RESULTS: Choline concentrations could be determined in 46 of 48 studies and was found to be 8.6 mmol per kilogram of wet weight±3.1 (range, 3.8-17.6; n=44). Twenty-seven spectra in 25 individuals with narrow line widths and low lipid content were adequate for quantitation of glycogen. The glycogen (glucosyl unit) concentration was 38.1 mmol/kg wet weight±14.4. The T2 of combined glycogen peaks in the liver of three subjects was 36 msec±8. Choline levels showed a weak but significant correlation with glycogen (r2=0.15; P<.05) but not with lipid content. CONCLUSION: Navigator-gated and gradient-echo shimmed PRESS 1H MR spectroscopy may allow quantification of liver metabolites that are important for understanding and identifying disorders of glucose and lipid metabolism.

Coronary vessel wall 3-T MR imaging with time-resolved acquisition of phase-sensitive dual inversion-recovery (TRAPD) technique: initial results in patients with risk factors for coronary artery disease.

PURPOSE: To develop a technique for time-resolved acquisition of phase-sensitive dual-inversion recovery (TRAPD) coronary vessel wall magnetic resonance (MR) images, to investigate the success rate in coronary wall imaging compared with that of single-frame imaging, and to assess vessel wall thickness in healthy subjects and subjects with risk factors for coronary artery disease (CAD). MATERIALS AND METHODS: Thirty-eight subjects (12 healthy subjects, 26 subjects with at least one CAD risk factor) provided informed consent for participation in this institutional review board-approved and HIPAA-compliant study. The TRAPD coronary vessel wall imaging sequence was developed and validated with a flow phantom. Time-resolved coronary artery wall images at three to five cine phases were obtained in all subjects. Qualitative and quantitative comparisons were made between TRAPD and conventional single-image wall measurements. Measurement reproducibility also was assessed. Statistical analysis was performed for all comparisons. RESULTS: The TRAPD sequence successfully restored the negative polarity of lumen signal and enhanced lumen wall contrast on the cine images of the flow phantom and in all subjects. Use of three to five frames increased the success rate of acquiring at least one image of good to excellent quality from 76% in single-image acquisitions to 95% with the TRAPD sequence. The difference in vessel wall thickness between healthy subjects and subjects with CAD risk factors was significant (P < .05) with the TRAPD sequence (1.07 vs 1.46 mm, respectively; 36% increase) compared with single-frame dual inversion-recovery imaging (1.24 vs 1.55 mm, respectively; 25% increase). Intraobserver, interobserver, and interexamination agreement for wall thickness measurement were 0.98, 0.97, and 0.92, respectively. CONCLUSION: TRAPD imaging of coronary arteries improved arterial wall visualization and quantitative assessment by increasing the success rate of obtaining good- to excellent-quality images and sections orthogonal to the longitudinal axis of the vessel. This also resulted in vessel wall thickness measurements that show a more distinct difference between healthy subjects and those with CAD risk factors. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120068/-/DC1.

Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT.

This Special Report presents the consensus of the Summit on Management of Radiation Dose in Computed Tomography (CT) (held in February 2011), which brought together participants from academia, clinical practice, industry, and regulatory and funding agencies to identify the steps required to reduce the effective dose from routine CT examinations to less than 1 mSv. The most promising technologies and methods discussed at the summit include innovations and developments in x-ray sources; detectors; and image reconstruction, noise reduction, and postprocessing algorithms. Access to raw projection data and standard data sets for algorithm validation and optimization is a clear need, as is the need for new, clinically relevant metrics of image quality and diagnostic performance. Current commercially available techniques such as automatic exposure control, optimization of tube potential, beam-shaping filters, and dynamic z-axis collimators are important, and education to successfully implement these methods routinely is critically needed. Other methods that are just becoming widely available, such as iterative reconstruction, noise reduction, and postprocessing algorithms, will also have an important role. Together, these existing techniques can reduce dose by a factor of two to four. Technical advances that show considerable promise for additional dose reduction but are several years or more from commercial availability include compressed sensing, volume of interest and interior tomography techniques, and photon-counting detectors. This report offers a strategic roadmap for the CT user and research and manufacturer communities toward routinely achieving effective doses of less than 1 mSv, which is well below the average annual dose from naturally occurring sources of radiation.

At the nexus of science, engineering, and medicine: Pasteur's quadrant reconsidered.

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.

Blood Pressure Estimation using a Single Channel Bio-Impedance Ring Sensor.

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.

Dynamic Tumor-Specific MHC-II Immuno-PET Predicts the Efficacy of Checkpoint Inhibitor Immunotherapy in Melanoma.

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.

Coronary artery abnormalities in Hyper-IgE syndrome.

OBJECTIVE: Hyper-IgE syndrome (HIES) is a rare primary immunodeficiency caused by autosomal dominant STAT3 mutations resulting in recurrent infections and connective tissue abnormalities. Coronary artery abnormalities have been reported infrequently. We aimed to determine the frequency and characteristics of coronary artery abnormalities. DESIGN: STAT3-mutated HIES patients (n=38), ranging in age from 8 to 57 years, underwent coronary artery imaging by computed tomography or magnetic resonance imaging. Images were evaluated for tortuosity, dilation, and aneurysm. Charts were reviewed for cardiac risk factors. To allow blinded image interpretation, an age- and gender-matched non-HIES group was also evaluated (n=33). RESULTS: Coronary artery tortuosity or dilation occurred in 70% of HIES patients, with aneurysms present in 37%, incidences much higher than in the literature and in our non-HIES group, in which 21% had tortuosity or dilation and 3% had aneurysms. Hypertension was more common in the HIES group than in the general population and was associated with vessel abnormalities. Atherosclerosis was uncommon and mild. CONCLUSIONS: Coronary artery aneurysms and tortuosity are common in HIES, despite a paucity of atherosclerosis, suggesting that STAT3 plays an integral role in human vascular remodeling and atherosclerosis.