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We investigated the ability of fluorodeoxyglucose positron emission tomography (FDG PET) imaging to serially monitor macrophage content in a rabbit model of atherosclerosis. Atherosclerosis was induced in rabbits (n = 8) by a combination of atherogenic diet and balloon denudation of the aorta. At the end of nine months, the rabbits were randomized to a further six months of the same atherogenic diet (progression group) or normal diet (regression group). In vivo uptake of FDG by the thoracic aorta was measured using aortic uptake-to-blood radioactivity ratios at the start and end of the randomized period. A significant increase in FDG uptake of the progression group after continued cholesterol feeding (aortic uptake-to-blood radioactivity: 0.57 +/- 0.02 to 0.68 +/- 0.02, P = 0.001), and a corresponding fall in FDG uptake of the regression group after returning to a normal chow diet (aortic uptake-to-blood radioactivity ratios: 0.67 +/- 0.02 to 0.53 +/- 0.02, P < 0.0001). FDG PET can quantify in vivo macrophage content and serially monitor changes in FDG activity in this rabbit model. [ABSTRACT FROM AUTHOR]

Abstract Background Atherosclerosis is a progressive disease that causes vascular remodeling that can be positive or negative. The evolution of arterial wall thickening and changes in lumen size under current "standard of care" in different arterial beds is unclear. The purpose of this study was to examine arterial remodeling and progression/regression of atherosclerosis in aorta and carotid arteries of individuals at risk for atherosclerosis normalized over a 1-year period. Methods In this study, 28 patients underwent at least 2 black-blood in vivo cardiovascular magnetic resonance (CMR) scans of aorta and carotids over a one-year period (Mean 17.8 ± 7.5 months). Clinical risk profiles for atherosclerosis and medications were documented and patients were followed by their referring physicians under current "standard of care" guidelines. Carotid and aortic wall lumen areas were matched across the time-points from cross-sectional images. Results The wall area increased by 8.67%, 10.64%, and 13.24% per year (carotid artery, thoracic aorta and abdominal aorta respectively, p < 0.001). The lumen area of the abdominal aorta increased by 4.97% per year (p = 0.002), but the carotid artery and thoracic aorta lumen areas did not change significantly. The use of statin therapy did not change the rate of increase of wall area of carotid artery, thoracic and abdominal aorta, but decreased the rate of change of lumen area of carotid artery (-3.08 ± 11.34 vs. 0.19 ± 12.91 p < 0.05). Conclusions Results of this study of multiple vascular beds indicated that different vascular locations exhibited varying progression of atherosclerosis and remodeling as monitored by CMR.

Abstract Aims Patients with prior major cardiovascular or cerebrovascular events (MACE) are more likely to have future recurrent events independent of traditional cardiovascular disease risk factors. The purpose of this study was to determine if patients with traditional risk factors and prior MACE had increased cardiovascular magnetic resonance (CMR) plaque burden measures compared to patients with risk factors but no prior events. Methods and Results Black blood carotid and thoracic aorta images were obtained from 195 patients using a rapid extended coverage turbo spin echo sequence. CMR measures of plaque burden were obtained by tracing lumen and outer vessel wall contours. Patients with prior MACE had significantly higher MR plaque burden (wall thickness, wall area and normalized wall index) in carotids and thoracic aorta compared to those without prior MACE (Wall thickness carotids: 1.03 ± 0.03 vs. 0.93± 0.03, p = 0.001; SD wall thickness carotids: 0.137 ± 0.0008 vs. 0.102 ± 0.0004, p < 0.001; wall thickness aorta: 1.63 ± 0.10 vs. 1.50 ± 0.04, p = 0.009; SD wall thickness aorta: 0.186 ± 0.035 vs. 0.139 ± 0.012, p = 0.009 respectively). Plaque burden (wall thickness) and plaque eccentricity (standard deviation of wall thickness) of carotid arteries were associated with prior MACE after adjustment for age, sex, and traditional risk factors. Area under ROC curve (AUC) for discriminating prior MACE improved by adding plaque eccentricity to models incorporating age, sex, and traditional CVD risk factors as model inputs (AUC = 0.79, p = 0.05). Conclusion A greater plaque burden and plaque eccentricity is prevalent among patients with prior MACE.

Objectives: The purpose of this study was to investigate the role of shear stress (SS) in plaque regression. Background: A condition favorable to the development of atherosclerotic lesions is low oscillating SS. In the descending thoracic aorta, the relationship between plaque distribution and SS has never been characterized. The regression of plaque as the result of lipid-lowering therapy is associated with reverse atherogenic mechanisms. Therefore, we investigated the role of SS in plaque regression. Magnetic resonance imaging (MRI) provides a unique opportunity to noninvasively study morphology and hemodynamics. Methods: Cross-sectional images of atherosclerotic plaques in the descending thoracic aorta of 10 asymptomatic, hypercholesteremic patients were acquired at baseline and 24 months after starting lipid-lowering therapy by using a black-blood sequence on a 1.5-T clinical MRI system (5 mm x 780 microm x 780 microm). Average wall thickness (WT) was derived per quadrant. The aorta was subdivided in segments 2 cm in length starting 1 cm from the aortic arch. Results: Average WT decreased with increasing distance from the arch (3.0 +/- 0.7 mm vs. 2.5 +/- 0.3 mm; p < 0.05) and showed a helical pattern from the proximal to distal segments. Phase-contrast MRI was performed in the thoracic aorta of eight healthy volunteers to derive typical average SS distribution. Shear stress predicted the location of WT (r(2) = 0.29, p < 0.05) but did not predict plaque regression. The best predictor of plaque regression was baseline WT. Conclusions: Our data showing an association between WT and average low SS locations support the role of local hemodynamics in the development of atherosclerotic lesions in descending thoracic aorta. Furthermore, SS does not seem to be the major predictor for plaque regression by lipid-lowering interventions. Therefore, our data suggest that other mechanisms are involved in the lipid-reversal mechanism. [ABSTRACT FROM AUTHOR]

Objectives: In this study, the impact of noninsulin-dependent type 2 diabetes mellitus on carotid wall (18)F-fluorodeoxyglucose (FDG) uptake in patients with documented or suspected cardiovascular disease was evaluated.Background: Inflammation is a pivotal process in the progression of atherosclerosis, which can be noninvasively imaged by FDG positron emission tomography (FDG-PET).Methods: Carotid artery wall FDG uptake was quantified in 134 patients (age 60.2 ± 9.7 years; diabetic subjects, n = 43). The pre-scan glucose (gluc) level corrected mean of the maximum standardized uptake value (SUV) values ((mean)SUV(gluc)), mean of the maximum target-to-background ratio ((mean)TBR(gluc)), and single hottest segment (SHS(gluc)) of FDG uptake in the artery wall were calculated. Associations between FDG uptake, the presence of risk factors for atherosclerosis, and diabetes were then assessed by multiple regression analysis with backward elimination.Results: The study demonstrated a significant association between diabetes and FDG uptake in the arterial wall (diabetes (mean)SUV(gluc) β = 0.324, (mean)TBR(gluc) β = 0.317, and SHS(gluc) β = 0.298; for all, p < 0.0001). In addition, in diabetic patients, both body mass index ≥ 30 kg/m(2) ((mean)SUV(gluc) β = 0.4, (mean)TBR(gluc) β = 0.357, and SHS(gluc) β = 0.388; for all, p < 0.015) and smoking ((mean)TBR(gluc), β = 0.312; SHS(gluc), β = 0.324; for all, p < 0.04) were significantly associated with FDG uptake.Conclusions: Type 2 diabetes was significantly associated with carotid wall FDG uptake in patients with known or suspected cardiovascular disease. In diabetic patients, obesity and smoking add to the risk of increased FDG uptake values. [ABSTRACT FROM AUTHOR]

Objectives: This study tested the near-term reproducibility of (18)fluorodeoxyglucose positron emission tomography (FDG-PET) imaging of atherosclerosis. Background: It is known that FDG-PET can measure inflammation within the aorta, carotid, and vertebral arteries with histologic validation in humans and animal models of disease. By tracking changes in inflammation over time, PET could be used as a surrogate marker of antiatheroma drug efficacy. However, the short-term variability and reproducibility of the technique are unknown. Methods: We imaged the carotid arteries and aorta in 11 subjects with FDG-PET/computed tomography twice, 14 days apart. We assessed interobserver and intraobserver agreement and interscan variability. Results: Interscan plaque FDG variability over 2 weeks was very low; intraclass correlation coefficients (ICC) ranged between 0.79 and 0.92. Interobserver agreement was high across all territories imaged except aortic arch (ICC values from 0.90 to 0.97, arch 0.71). Intraobserver agreement was high, with ICC values between 0.93 and 0.98. Conclusions: Spontaneous change in plaque FDG uptake is low over 2 weeks, with favorable inter- and intraobserver agreement. Power calculations suggest that drug studies using FDG-PET imaging would require few subjects compared with other imaging modalities. This study strengthens the case for FDG-PET as a noninvasive plaque imaging technique. [ABSTRACT FROM AUTHOR]

Background: 18 F-FDG-PET/MR can identify inflammation and fibrosis, high-risk features in cardiac sarcoidosis., Objective: To evaluate whether the involvement of certain myocardial segments is associated with higher risk compared to others., Methods: 124 patients with suspected clinical sarcoidosis underwent 18 F-FDG-PET/MR. Late gadolinium enhancement (LGE) and focal 18 F-FDG uptake were evaluated globally and in the 16 myocardial segments. Presence of LGE was defined when the percentage of LGE exceeded 5.7% globally (relative to myocardial volume) and in each myocardial segment. Patients were followed up for 5.5 years. Events were defined as ventricular arrhythmia (VA, including sustained ventricular tachycardia, ventricular fibrillation, and appropriate ICD discharge), heart failure hospitalization, or all-cause death., Results: Mean age was 57.1±8.9 years, and 39.5% were female; 22 patients (17.6%) had an event at follow-up, and 9 (7.2%) presented VA. LGE and 18 F-FDG uptake were more frequent in patients with than without events (36.4% vs 7.8%, p=0.001). Presence of LGE or 18 F-FDG in the basal anterior segment were independent predictors for events after adjustment by left ventricular ejection fraction and relative enhanced volume (LGE: OR 10.5[1.2-92.4]; p=0.034; 18 F-FDG: OR 5.5[1.1-27.5], p=0.038). LGE presence in basal to mid anterior, mid anteroseptal, and basal to mid inferoseptal segments was an independent predictor for VA. Presence of 18 F-FDG in basal to mid anterior, mid inferoseptal and mid inferior segments was an independent predictor for VA., Conclusion: Involvement of specific myocardial segments, particularly basal to mid anterior and mid septal segments, is associated with higher rates of events in patients with suspected cardiac sarcoidosis., (Copyright © 2024. Published by Elsevier Inc.)

Assessing atherosclerosis severity is essential for precise patient stratification. Specifically, there is a need to identify patients with residual inflammation because these patients remain at high risk of cardiovascular events despite optimal management of cardiovascular risk factors. Molecular imaging techniques, such as PET, can have an essential role in this context. PET imaging can indicate tissue-based disease status, detect early molecular changes and provide whole-body information. Advances in molecular biology and bioinformatics continue to help to decipher the complex pathogenesis of atherosclerosis and inform the development of imaging tracers. Concomitant advances in tracer synthesis methods and PET imaging technology provide future possibilities for atherosclerosis imaging. In this Review, we summarize the latest developments in PET imaging techniques and technologies for assessment of atherosclerotic cardiovascular disease and discuss the relationship between imaging readouts and transcriptomics-based plaque phenotyping., (© 2024. Springer Nature Limited.)

The innate immune system plays an essential role in regulating the immune responses to kidney transplantation, but the mechanisms through which innate immune cells influence long-term graft survival are unclear. The current study highlights the vital role of trained immunity in kidney allograft survival. Trained immunity describes the epigenetic and metabolic changes that innate immune cells undergo following an initial stimulus, allowing them have a stronger inflammatory response to subsequent stimuli. We stimulated healthy peripheral blood mononuclear cells with pretransplant and posttransplant serum of kidney transplant patients and immunosuppressive drugs in an in vitro trained immunity assay and measured tumor necrosis factor and interleukin 6 cytokine levels in the supernatant as a readout for trained immunity. We show that the serum of kidney transplant recipients collected 1 week after transplantation can suppress trained immunity. Importantly, we found that kidney transplant recipients whose serum most strongly suppressed trained immunity rarely experienced graft loss. This suppressive effect of posttransplant serum is likely mediated by previously unreported effects of immunosuppressive drugs. Our findings provide mechanistic insights into the role of innate immunity in kidney allograft survival, uncovering trained immunity as a potential therapeutic target for improving graft survival., Competing Interests: Declaration of competing interest The authors of this manuscript have conflicts of interest to disclose as described by the American Journal of Transplantation. L. A. B. Joosten is scientific founder of TTxD, LembaTX, and SalvinaTX. M. G. Netea is scientific founder of TTxD and Biotrip. W. J. M. Mulder is scientific founder of TTxD and Biotrip. Other authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels., (© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Regulating innate immunity is an emerging approach to improve cancer immunotherapy. Such regulation requires engaging myeloid cells by delivering immunomodulatory compounds to hematopoietic organs, including the spleen. Here we present a polymersome-based nanocarrier with splenic avidity and propensity for red pulp myeloid cell uptake. We characterized the in vivo behaviour of four chemically identical yet topologically different polymersomes by in vivo positron emission tomography imaging and innovative flow and mass cytometry techniques. Upon intravenous administration, relatively large and spherical polymersomes accumulated rapidly in the spleen and efficiently targeted myeloid cells in the splenic red pulp. When loaded with β-glucan, intravenously administered polymersomes significantly reduced tumour growth in a mouse melanoma model. We initiated our nanotherapeutic's clinical translation with a biodistribution study in non-human primates, which revealed that the platform's splenic avidity is preserved across species., (© 2024. The Author(s).)

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.

Background and Aims: Chronic stress associates with cardiovascular disease, but mechanisms remain incompletely defined. Advanced imaging was used to identify stress-related neural imaging phenotypes associated with atherosclerosis., Methods: Twenty-seven individuals with post-traumatic stress disorder (PTSD), 45 trauma-exposed controls without PTSD, and 22 healthy controls underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI). Atherosclerotic inflammation and burden were assessed using 18F-FDG PET (as maximal target-to-background ratio, TBR max) and MRI, respectively. Inflammation was assessed using high-sensitivity C-reactive protein (hsCRP) and leucopoietic imaging (18F-FDG PET uptake in spleen and bone marrow). Stress-associated neural network activity (SNA) was assessed on 18F-FDG PET as amygdala relative to ventromedial prefrontal cortex (vmPFC) activity. MRI diffusion tensor imaging assessed the axonal integrity (AI) of the uncinate fasciculus (major white matter tract connecting vmPFC and amygdala)., Results: Median age was 37 years old and 54% of participants were female. There were no significant differences in atherosclerotic inflammation between participants with PTSD and controls; adjusted mean difference in TBR max (95% confidence interval) of the aorta 0.020 (-0.098, 0.138), and of the carotids 0.014 (-0.091, 0.119). Participants with PTSD had higher hsCRP, spleen activity, and aorta atherosclerotic burden (normalized wall index). Participants with PTSD also had higher SNA and lower AI. Across the cohort, carotid atherosclerotic burden (standard deviation of wall thickness) associated positively with SNA and negatively with AI independent of Framingham risk score., Conclusions: In this study of limited size, participants with PTSD did not have higher atherosclerotic inflammation than controls. Notably, impaired cortico-limbic interactions (higher amygdala relative to vmPFC activity or disruption of their intercommunication) associated with carotid atherosclerotic burden. Larger studies are needed to refine these findings., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach., Objective: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being., Methods: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV., Results: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant., Conclusions: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions., (©Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Helena Chang, Drew Helmus, Bruce E Sands, Dennis Charney, Girish Nadkarni, Emilia Bagiella, Laurie Keefer, Zahi A Fayad. Originally published in JMIR Mental Health (https://mental.jmir.org), 25.04.2024.)

Objectives: Rheumatoid arthritis (RA) and atherosclerosis share many common inflammatory pathways. We studied whether a multi-biomarker panel for RA disease activity (MBDA) would associate with changes in arterial inflammation in an interventional trial., Methods: In the TARGET Trial, RA patients with active disease despite methotrexate were randomly assigned to the addition of either a TNF inhibitor or sulfasalazine+hydroxychloroquine (triple therapy). Baseline and 24-week follow-up 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography scans were assessed for change in arterial inflammation measured as the maximal arterial target-to-blood background ratio of FDG uptake in the most diseased segment of the carotid arteries or aorta (MDS-TBRmax). The MBDA test, measured at baseline and weeks 6, 18, and 24, was assessed for its association with the change in MDS-TBRmax., Results: Interpretable scans were available at baseline and week 24 for n = 112 patients. The MBDA score at week 24 was significantly correlated with the change in MDR-TBRmax (Spearman's rho = 0.239; p= 0.011) and remained significantly associated after adjustment for relevant confounders. Those with low MBDA at week 24 had a statistically significant adjusted reduction in arterial inflammation of 0.35 units vs no significant reduction in those who did not achieve low MBDA. Neither DAS28-CRP nor CRP predicted change in arterial inflammation. The MBDA component with the strongest association with change in arterial inflammation was serum amyloid A (SAA)., Conclusions: Among treated RA patients, achieved MBDA predicts of changes in arterial inflammation. Achieving low MBDA at 24 weeks was associated with clinically meaningful reductions in arterial inflammation, regardless of treatment., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Imaging with late gadolinium enhancement (LGE) magnetic resonance (MR) and 18 F-fluorodeoxyglucose ( 18 F-FDG) PET allows complementary assessment of myocardial injury and disease activity and has shown promise for improved characterization of active cardiac sarcoidosis (CS) based on the combined positive imaging outcome, MR(+)PET(+)., Objectives: This study aims to evaluate qualitative and quantitative assessments of hybrid MR/PET imaging in CS and to evaluate its association with cardiac-related outcomes., Methods: A total of 148 patients with suspected CS underwent hybrid MR/PET imaging. Patients were classified based on the presence/absence of LGE (MR+/MR-), presence/absence of 18 F-FDG (PET+/PET-), and pattern of 18 F-FDG uptake (focal/diffuse) into the following categories: MR(+)PET(+) FOCAL , MR(+)PET(+) DIFFUSE , MR(+)PET(-), MR(-)PET(+) FOCAL , MR(-)PET(+) DIFFUSE , MR(-)PET(-). Further analysis classified MR positivity based on %LGE exceeding 5.7% as MR(+/-) 5.7% . Quantitative values of standard uptake value, target-to-background ratio, target-to-normal-myocardium ratio (TNMRmax), and T2 were measured. The primary clinical endpoint was met by the occurrence of cardiac arrest, ventricular tachycardia, or secondary prevention implantable cardioverter-defibrillator (ICD) before the end of the study. The secondary endpoint was met by any of the primary endpoint criteria plus heart failure or heart block. MR/PET imaging results were compared between those meeting or not meeting the clinical endpoints., Results: Patients designated MR(+) 5.7% PET(+) FOCAL had increased odds of meeting the primary clinical endpoint compared to those with all other imaging classifications (unadjusted OR: 9.2 [95% CI: 3.0-28.7]; P = 0.0001), which was higher than the odds based on MR or PET alone. TNMRmax achieved an area under the receiver-operating characteristic curve of 0.90 for separating MR(+)PET(+) FOCAL from non-MR(+)PET(+) FOCAL , and 0.77 for separating those reaching the clinical endpoint from those not reaching the clinical endpoint., Conclusions: Hybrid MR/PET image-based classification of CS was statistically associated with clinical outcomes in CS. TNMRmax had modest sensitivity and specificity for quantifying the imaging-based classification MR(+)PET(+) FOCAL and was associated with outcomes. Use of combined MR and PET image-based classification may have use in prognostication and treatment management in CS., Competing Interests: Funding Support and Author Disclosures This work was supported by National Institutes of Health (NIH) grant R01 HL071021 (ZAF), NIH grant KL2 TR001435 (MGT), and AHA grant 20CDA35310099 (MGT). The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Nanomaterials have revolutionized medicine by enabling control over drugs' pharmacokinetics, biodistribution, and biocompatibility. However, most nanotherapeutic batches are highly heterogeneous, meaning they comprise nanoparticles that vary in size, shape, charge, composition, and ligand functionalization. Similarly, individual nanotherapeutics often have heterogeneously distributed components, ligands, and charges. This review discusses nanotherapeutic heterogeneity's sources and effects on experimental readouts and therapeutic efficacy. Among other topics, it demonstrates that heterogeneity exists in nearly all nanotherapeutic types, examines how nanotherapeutic heterogeneity arises, and discusses how heterogeneity impacts nanomaterials' in vitro and in vivo behavior. How nanotherapeutic heterogeneity skews experimental readouts and complicates their optimization and clinical translation is also shown. Lastly, strategies for limiting nanotherapeutic heterogeneity are reviewed and recommendations for developing more reproducible and effective nanotherapeutics provided., (© 2023 Wiley‐VCH GmbH.)

Background: Previous retrospective studies suggest a good diagnostic performance of 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET)/computed tomography (CT) in left ventricular assist device (LVAD) infections. Our aim was to prospectively evaluate the role of PET/CT in the characterization and impact on clinical management of LVAD infections., Methods: A total of 40 patients (aged 58 [53-62] years) with suspected LVAD infection and 5 controls (aged 69 [64-71] years) underwent 18 F-FDG-PET/CT. Four LVAD components were evaluated: exit site and subcutaneous driveline (peripheral), pump pocket, and outflow graft. The location with maximal uptake was considered the presumed site of infection. Infection was confirmed by positive culture (exit site or blood) and/or surgical findings., Results: Visual uptake was present in 40 patients (100%) in the infection group vs 4 (80%) control subjects. For each individual component, the presence of uptake was more frequent in the infection than in the control group. The location of maximal uptake was most frequently the pump pocket (48%) in the infection group and the peripheral components (75%) in the control group. Maximum standard uptake values (SUV max ) were higher in the infection than in the control group: SUV max (average all components): 6.9 (5.1-8.5) vs 3.8 (3.7-4.3), p = 0.002; SUV max (location of maximal uptake): 10.6 ± 4.0 vs 5.4 ± 1.9, p = 0.01. Pump pocket infections were more frequent in patients with bacteremia than without bacteremia (79% vs 31%, p = 0.011). Pseudomonas (32%) and methicillin-susceptible Staphylococcus aureus (29%) were the most frequent pathogens and were associated with pump pocket infections, while Staphylococcus epidermis (11%) was associated with peripheral infections. PET/CT affected the clinical management of 83% of patients with infection, resulting in surgical debridement (8%), pump exchange (13%), and upgrade in the transplant listing status (10%), leading to 8% of urgent transplants., Conclusions: 18 F-FDG-PET/CT enables the diagnosis and characterization of the extent of LVAD infections, which can significantly affect the clinical management of these patients., (Copyright © 2023 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

While posttraumatic stress disorder (PTSD) is known to associate with an elevated risk for major adverse cardiovascular events (MACE), few studies have examined mechanisms underlying this link. Recent studies have demonstrated that neuro-immune mechanisms, (manifested by heightened stress-associated neural activity (SNA), autonomic nervous system activity, and inflammation), link common stress syndromes to MACE. However, it is unknown if neuro-immune mechanisms similarly link PTSD to MACE. The current study aimed to test the hypothesis that upregulated neuro-immune mechanisms increase MACE risk among individuals with PTSD. This study included N = 118,827 participants from a large hospital-based biobank. Demographic, diagnostic, and medical history data collected from the biobank. SNA (n = 1,520), heart rate variability (HRV; [n = 11,463]), and high sensitivity C-reactive protein (hs-CRP; [n = 15,164]) were obtained for a subset of participants. PTSD predicted MACE after adjusting for traditional MACE risk factors (hazard ratio (HR) [95 % confidence interval (CI)] = 1.317 [1.098, 1.580], β = 0.276, p = 0.003). The PTSD-to-MACE association was mediated by SNA (CI = 0.005, 0.133, p < 0.05), HRV (CI = 0.024, 0.056, p < 0.05), and hs-CRP (CI = 0.010, 0.040, p < 0.05). This study provides evidence that neuro-immune pathways may play important roles in the mechanisms linking PTSD to MACE. Future studies are needed to determine if these markers are relevant targets for PTSD treatment and if improvements in SNA, HRV, and hs-CRP associate with reduced MACE risk in this patient population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Macrophages are key inflammatory mediators in many pathological conditions, including cardiovascular disease (CVD) and cancer, the leading causes of morbidity and mortality worldwide. This makes macrophage burden a valuable diagnostic marker and several strategies to monitor these cells have been reported. However, such strategies are often high-priced, non-specific, invasive, and/or not quantitative. Here, we developed a positron emission tomography (PET) radiotracer based on apolipoprotein A1 (ApoA1), the main protein component of high-density lipoprotein (HDL), which has an inherent affinity for macrophages. We radiolabeled an ApoA1-mimetic peptide (mA1) with zirconium-89 ( 89 Zr) to generate a lipoprotein-avid PET probe ( 89 Zr-mA1). We first characterized 89 Zr-mA1's affinity for lipoproteins in vitro by size exclusion chromatography. To study 89 Zr-mA1's in vivo behavior and interaction with endogenous lipoproteins, we performed extensive studies in wildtype C57BL/6 and Apoe -/- hypercholesterolemic mice. Subsequently, we used in vivo PET imaging to study macrophages in melanoma and myocardial infarction using mouse models. The tracer's cell specificity was assessed by histology and mass cytometry (CyTOF). Our data show that 89 Zr-mA1 associates with lipoproteins in vitro. This is in line with our in vivo experiments, in which we observed longer 89 Zr-mA1 circulation times in hypercholesterolemic mice compared to C57BL/6 controls. 89 Zr-mA1 displayed a tissue distribution profile similar to ApoA1 and HDL, with high kidney and liver uptake as well as substantial signal in the bone marrow and spleen. The tracer also accumulated in tumors of melanoma-bearing mice and in the ischemic myocardium of infarcted animals. In these sites, CyTOF analyses revealed that nat Zr-mA1 was predominantly taken up by macrophages. Our results demonstrate that 89 Zr-mA1 associates with lipoproteins and hence accumulates in macrophages in vivo. 89 Zr-mA1's high uptake in these cells makes it a promising radiotracer for non-invasively and quantitatively studying conditions characterized by marked changes in macrophage burden., Competing Interests: Competing interestsW.J.M.M. is a founder and CSO of Trained Therapeutix Discovery. Z.A.F. is a founder and member of the board of Trained Therapeutix Discovery. T.R. is now an employee of and shareholder in Novartis AG. No other potential financial or non-financial conflicts of interest relevant to this article exist., (© The Author(s) 2024.)

Innate immune memory, also called "trained immunity," is a functional state of myeloid cells enabling enhanced immune responses. This phenomenon is important for host defense, but also plays a role in various immune-mediated conditions. We show that exogenously administered sphingolipids and inhibition of sphingolipid metabolizing enzymes modulate trained immunity. In particular, we reveal that acid ceramidase, an enzyme that converts ceramide to sphingosine, is a potent regulator of trained immunity. We show that acid ceramidase regulates the transcription of histone-modifying enzymes, resulting in profound changes in histone 3 lysine 27 acetylation and histone 3 lysine 4 trimethylation. We confirm our findings by identifying single-nucleotide polymorphisms in the region of ASAH1, the gene encoding acid ceramidase, that are associated with the trained immunity cytokine response. Our findings reveal an immunomodulatory effect of sphingolipids and identify acid ceramidase as a relevant therapeutic target to modulate trained immunity responses in innate immune-driven disorders., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform's "Training Module". The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings.

Background: The main advantage of ultra-high field (UHF) magnetic resonance neuroimaging is theincreased signal-to-noise ratio (SNR) compared with lower field strength imaging. However, the wavelength effect associated with UHF MRI results in radiofrequency (RF) inhomogeneity, compromising whole brain coverage for many commercial coils. Approaches to resolving this issue of transmit field inhomogeneity include the design of parallel transmit systems (PTx), RF pulse design, and applying passive RF shimming such as high dielectric materials. However, these methods have some drawbacks such as unstable material parameters of dielectric pads, high-cost, and complexity of PTx systems. Metasurfaces are artificial structures with a unique platform that can control the propagation of the electromagnetic (EM) waves, and they are very promising for engineering EM device. Implementation of meta-arrays enhancing MRI has been explored previously in several studies., Purpose: The aim of this study was to assess the effect of new meta-array technology on enhancing the brain MRI at 7T. A meta-array based on a hybrid structure consisting of an array of broadside-coupled split-ring resonators and high-permittivity materials was designed to work at the Larmor frequency of a 7 Tesla (7T) MRI scanner. When placed behind the head and neck, this construct improves the SNR in the region of the cerebellum,brainstem and the inferior aspect of the temporal lobes., Methods: Numerical electromagnetic simulations were performed to optimize the meta-array design parameters and determine the RF circuit configuration. The resultant transmit-efficiency and signal sensitivity improvements were experimentally analyzed in phantoms followed by healthy volunteers using a 7T whole-body MRI scanner equipped with a standard one-channel transmit, 32-channel receive head coil. Efficacy was evaluated through acquisition with and without the meta-array using two basic sequences: gradient-recalled-echo (GRE) and turbo-spin-echo (TSE)., Results: Experimental phantom analysis confirmed two-fold improvement in the transmit efficiency and 1.4-fold improvement in the signal sensitivity in the target region. In vivo GRE and TSE images with the meta-array in place showed enhanced visualization in inferior regions of the brain, especially of the cerebellum, brainstem, and cervical spinal cord., Conclusion: Addition of the meta-array to commonly used MRI coils can enhance SNR to extend the anatomical coverage of the coil and improve overall MRI coil performance. This enhancement in SNR can be leveraged to obtain a higher resolution image over the same time slot or faster acquisition can be achieved with same resolution. Using this technique could improve the performance of existing commercial coils at 7T for whole brain and other applications., (© 2023 American Association of Physicists in Medicine.)

Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development., Objective: This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms., Methods: The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided., Results: The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs., Conclusions: Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs., International Registered Report Identifier (irrid): DERR1-10.2196/49204., (©Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Georgy Orlov, Jovita Rodrigues, Eugenia Alleva, Ipek Ensari, Erwin Bottinger, Girish N Nadkarni, Thomas J Fuchs, Zahi A Fayad. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 16.11.2023.)

Background: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV., Objective: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City., Methods: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well., Results: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F 1 -score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance., Conclusions: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19., (©Kim-Anh-Nhi Nguyen, Pranai Tandon, Sahar Ghanavati, Satya Narayana Cheetirala, Prem Timsina, Robert Freeman, David Reich, Matthew A Levin, Madhu Mazumdar, Zahi A Fayad, Arash Kia. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.10.2023.)

The current standard for measuring coronary artery calcification to determine the extent of atherosclerosis is by calculating the Agatston score from computed tomography (CT). However, the Agatston score disregards pixel values less than 130 Hounsfield Units (HU) and calcium regions less than 1 mm 2 . Due to this thresholding, the score is not sensitive to small, weakly attenuating regions of calcium deposition and may not detect nascent micro-calcification. A recently proposed metric called the spatially weighted calcium score (SWCS) also utilizes CT but does not include a threshold for HU and does not require elevated signals in contiguous pixels. Thus, the SWCS is sensitive to weakly attenuating, smaller calcium deposits and may improve the measurement of coronary heart disease risk. Currently, the SWCS is underutilized owing to the added computational complexity. To promote translation of the SWCS into clinical research and reliable, repeatable computation of the score, the aim of this study was to develop a semi-automatic graphical tool that calculates both the SWCS and the Agatston score. The program requires gated cardiac CT scans with a calcium hydroxyapatite phantom in the field of view. The phantom allows for deriving a weighting function, from which each pixel's weight is adjusted, allowing for the mitigation of signal variations and variability between scans. With all three anatomical views visible simultaneously, the user traces the course of the four main coronary arteries by placing points or regions of interest. Features such as scroll-to-zoom, double-click to delete, and brightness/contrast adjustment, along with written guidance at every step, make the program user-friendly and easy to use. Once tracing the arteries is complete, the program generates reports, which include the scores and snapshots of any visible calcium. The SWCS may reveal the presence of subclinical disease, which may be used for early intervention and lifestyle changes.

Immunoparalysis is a compensatory and persistent anti-inflammatory response to trauma, sepsis or another serious insult, which increases the risk of opportunistic infections, morbidity and mortality. Here, we show that in cultured primary human monocytes, interleukin-4 (IL4) inhibits acute inflammation, while simultaneously inducing a long-lasting innate immune memory named trained immunity. To take advantage of this paradoxical IL4 feature in vivo, we developed a fusion protein of apolipoprotein A1 (apoA1) and IL4, which integrates into a lipid nanoparticle. In mice and non-human primates, an intravenously injected apoA1-IL4-embedding nanoparticle targets myeloid-cell-rich haematopoietic organs, in particular, the spleen and bone marrow. We subsequently demonstrate that IL4 nanotherapy resolved immunoparalysis in mice with lipopolysaccharide-induced hyperinflammation, as well as in ex vivo human sepsis models and in experimental endotoxemia. Our findings support the translational development of nanoparticle formulations of apoA1-IL4 for the treatment of patients with sepsis at risk of immunoparalysis-induced complications., (© 2023. The Author(s).)