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Mary A Dolansky,1 Julie T Schaefer,2 Misty AW Hawkins,3 John Gunstad,2 Anup Basuray,4 Joseph D Redle,5 James C Fang,6 Richard A Josephson,7,8 Shirley M Moore,1 Joel W Hughes2 1School of Nursing, Case Western Reserve University, Cleveland, OH, USA; 2Department of Psychology, Kent State University, Kent, OH, USA; 3Department of Psychology, Oklahoma State University, Stillwater, OK, USA; 4Ohio Health Heart and Vascular Physicians, Columbus, OH, USA; 5Department of Cardiology, Summa Health System, Akron City Hospital, OH, USA; 6Department of Internal Medicine, Division of Cardiology, University Hospital, Salt Lake City, UT, USA; 7Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; 8Harrington Heart & Vascular Institute, University Hospitals, Cleveland, OH, USA Background: Although cognitive impairment is common in heart failure (HF) patients, its effects on sodium adherence recommendations are unknown. Purpose: Our aim is to examine if cognitive function is associated with patient sodium adherence. Methods: Sodium collection/excretion and cognitive function were assessed for 339 HF patients over a 5–8-week period. Neuropsychological testing was performed at baseline (Visit 1), whereas two 24-hour urine samples were collected within 7 weeks postbaseline. The ability to collect two 24-hour urine samples and the estimation of sodium excretion levels from these samples were used to estimate sodium adherence recommendations. Results: Nearly half (47%) of the study participants (n=159) were unable to give two valid 24-hour urine samples. Participants who were unable to adhere to two valid 24-hour urine samples had significantly poorer attention and global cognition tests (P

The U.S. Fusion Energy Sciences Advisory Committee was charged “to identify the most promising transformative enabling capabilities (TEC) for the U.S. to pursue that could promote efficient advance toward fusion energy, building on burning plasma science and technology.” A subcommittee of U.S. technical experts was formed and received community input in the form of white papers and presentations on the charge questions. The subcommittee identified four “most promising transformative enabling capabilities”: 1. advanced algorithms 2. high critical temperature superconductors 3. advanced materials and manufacturing 4. novel technologies for tritium fuel cycle control. In addition, one second-tier TEC, defined as a “promising transformative enabling capability,” was identified: fast-flowing liquid-metal plasma-facing components. Each of these TECs presents a tremendous opportunity to accelerate fusion science and technology toward power production. Dedicated investment in these TECs for fusion systems is needed to capitalize on the rapid advances being made for a variety of nonfusion applications to fully realize their transformative potential for fusion energy.

A comprehensive study of fully frequency-resolved nonlinear kinetic energy transfer has been performed for the first time in a diverted tokamak, providing new insight into the parametric dependences of edge turbulence transitions. Measurements using gas puff imaging in the turbulent L-mode state illuminate the source of the long known but as yet unexplained "favorable-unfavorable" geometric asymmetry of the power threshold for transition to the turbulence-suppressed H mode. Results from the recently discovered I mode point to a competition between zonal flow (ZF) and geodesic-acoustic modes (GAM) for turbulent energy, while showing new evidence that the I-to-H transition is still dominated by ZFs. The availability of nonlinear drive for the GAM against net heat flux through the edge corresponds very well to empirical scalings found experimentally for accessing the I mode.

Nonlinear transfer processes between large-scale edge flows and the ambient broadband fluctuations have been shown to play a significant role in the dynamics of edge turbulence, including spreading power from coherent modes and suppressing turbulence at the formation of edge transport barriers. In order to predict thresholds of confinement regimes, both the transition dynamics and the parametric dependence of the nonlinear energy transfer must be studied. Since the expected flow damping terms depend on ion collision rates and local safety factors, recent experiments aimed also to explore the nonlinear drive at various values of the plasma current, density and amount of auxiliary heating. Nonlinear interactions between zonal flows and turbulence in L-mode are estimated using bispectral as well as time-resolved methods based on gas-puff-imaging in Alcator C-Mod [1].

Transitions of tokamak confinement regimes from low- to high-confinement are studied on Alcator C-Mod (Hutchinson et al 1994 Phys. Plasmas 1 1511) tokamak using gas-puff-imaging, with a focus on the interaction between the edge drift-turbulence and the local shear flow. Results show that the nonlinear turbulent kinetic energy transfer rate into the shear flow becomes comparable to the estimated value of the drift turbulence growth rate at the time the turbulent kinetic energy starts to drop, leading to a net energy transfer that is comparable to the observed turbulence losses. A corresponding growth is observed in the shear flow kinetic energy. The above behavior is demonstrated across a series of experiments. Thus both the drive of the edge zonal flow and the initial reduction of turbulence fluctuation power are shown to be consistent with a lossless kinetic energy conversion mechanism, which consequently mediates the transition into H-mode. The edge pressure gradient is then observed to build on a slower (1ms) timescale, locking in the H-mode state. These results unambiguously establish the time sequence of the transition as: first the peaking of the normalized Reynolds power, then the collapse of the turbulence, and finally the rise of the diamagnetic electric field shear as the L-H transition occurs. © 2014 IOP Publishing Ltd.

Application of lower hybrid (LH) current drive in tokamak plasmas can induce both co- and countercurrent directed changes in toroidal rotation, depending on the core q profile. For discharges with q(0)

Joint experiment/theory/modelling research has led to increased confidence in predictions of the pedestal height in ITER. This work was performed as part of a US Department of Energy Joint Research Target in FY11 to identify physics processes that control the H-mode pedestal structure. The study included experiments on C-Mod, DIII-D and NSTX as well as interpretation of experimental data with theory-based modelling codes. This work provides increased confidence in the ability of models for peeling-ballooning stability, bootstrap current, pedestal width and pedestal height scaling to make correct predictions, with some areas needing further work also being identified. A model for pedestal pressure height has made good predictions in existing machines for a range in pressure of a factor of 20. This provides a solid basis for predicting the maximum pedestal pressure height in ITER, which is found to be an extrapolation of a factor of 3 beyond the existing data set. Models were studied for a number of processes that are proposed to play a role in the pedestal ne and Te profiles. These processes include neoclassical transport, paleoclassical transport, electron temperature gradient turbulence and neutral fuelling. All of these processes may be important, with the importance being dependent on the plasma regime. Studies with several electromagnetic gyrokinetic codes show that the gradients in and on top of the pedestal can drive a number of instabilities. © 2013 IAEA, Vienna.

Several seemingly unrelated effects in Alcator C-Mod ohmic L-mode plasmas are shown to be closely connected: non-local heat transport, core toroidal rotation reversals, energy confinement saturation and up/down impurity density asymmetries. These phenomena all abruptly transform at a critical value of the collisionality. At low densities in the linear ohmic confinement regime, with collisionality ν* 0.35 (evaluated inside of the q = 3/2 surface), heat transport exhibits non-local behaviour, core toroidal rotation is directed co-current, edge impurity density profiles are up/down symmetric and a turbulent feature in core density fluctuations with kθ up to 15 cm-1 (kθρs ∼ 1) is present. At high density/collisionality with saturated ohmic confinement, electron thermal transport is diffusive, core rotation is in the counter-current direction, edge impurity density profiles are up/down asymmetric and the high k θ turbulent feature is absent. The rotation reversal stagnation point (just inside of the q = 3/2 surface) coincides with the non-local electron temperature profile inversion radius. All of these observations suggest a possible unification in a model with trapped electron mode prevalence at low collisionality and ion temperature gradient mode domination at high collisionality. © 2013 IAEA, Vienna.

Ohmic energy confinement saturation is found to be closely related to core toroidal rotation reversals in Alcator C-Mod tokamak plasmas. Rotation reversals occur at a critical density, depending on the plasma current and toroidal magnetic field, which coincides with the density separating the linear Ohmic confinement regime from the saturated Ohmic confinement regime. The rotation is directed co-current at low density and abruptly changes direction to counter-current when the energy confinement saturates as the density is increased. Since there is a bifurcation in the direction of the rotation at this critical density, toroidal rotation reversal is a very sensitive indicator in the determination of the regime change. The reversal and confinement saturation results can be unified, since these processes occur in a particular range of the collisionality.

Direction reversals of intrinsic toroidal rotation have been observed in diverted Alcator C-Mod Ohmic L-mode plasmas following electron density ramps. For low density discharges, the core rotation is directed cocurrent, and reverses to countercurrent following an increase in the density above a certain threshold. Such reversals occur together with a decrease in density fluctuations with 2 cm(-1)≤k(θ)≤11 cm(-1) and frequencies above 70 kHz. There is a strong correlation between the reversal density and the density at which the Ohmic L-mode energy confinement changes from the linear to the saturated regime.

Reduced adherence to medical treatment regimens may help to explain the higher risk of mortality among depressed cardiac patients. Participation in cardiac rehabilitation is a highly recommended part of the medical treatment regimen for cardiac patients. This study examined if elevated depressive symptomology, as measured by the Beck Depression Inventory (BDI), predicted failure to complete a 12-week phase II cardiac rehabilitation program for 600 patients. Logistic regression analysis showed that patients with elevated levels of depressive symptomology (BDI scores >/= 10) were 2.2 times less likely to complete cardiac rehabilitation compared to patients without depression (BDI < 10), after controlling for age, gender, body mass index, and employment. Somatic symptoms predicted non-completion due to medical reasons, whereas younger age predicted failure to complete cardiac rehabilitation due to non-medical reasons. Given the difficulty of reducing mortality by treating depression directly, interventions targeting behavior change to improve medical treatment adherence might be an effective complementary strategy. [ABSTRACT FROM AUTHOR]

Telehealth utilization has increased since the coronavirus disease 2019 pandemic, reducing barriers to healthcare and, potentially, reducing participation in group health-promotion interventions. However, preferences for telehealth versus in-person formats have not been established. To examine preferences for telehealth and in-person format for primary care and group health-promotion interventions among Black and White women and men aged 20-39. We hypothesized that respondents would report a higher preference for telehealth than in-person appointments. This cross-sectional survey study recruited participants to answer questions about access to technology and preferences for telehealth and in-person formats of primary care and group health promotion. Respondents (n = 404) included similar proportions of White women (24.3%, 26.4 ± 4.3 years), Black women (25.0%, 29.0 ± 6.1 years), White men (25.9%, 32.8 ± 4.5 years), and Black men (24.8%, 30.6 ± 5.2 years). About 98.5% reported having a smartphone, and 80.4% had access to a computer with a camera. Preference ratings were higher for in-person visits, compared to telehealth visits, for both primary care (M = 3.86 ± 1.13 vs. M = 2.87 ± 1.18) and group health promotion (M = 3.72 ± 1.12 vs. M = 3.04 ± 1.20) F's(1,400) > 59.0, P's < .001. Most young adults have access to technology, supporting the feasibility of telehealth interventions. However, telehealth preference ratings were lower than in-person appointments. Preferences for delivery formats should be considered when designing behavioral interventions to promote health and prevent disease., (© Society of Behavioral Medicine 2024. 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.)

The definitive demonstration of protein localization on primary cilia has been a challenge for cilia biologists. Primary cilia are solitary thread-like projections that have a specialized protein composition, but as the ciliary structure overlays the cell membrane and other cell parts, the identity of ciliary proteins are difficult to ascertain by conventional imaging approaches like immunofluorescence microscopy. Surface scanning electron microscopy combined with immunolabeling (immuno-SEM) bypasses some of these indeterminacies by unambiguously showing protein expression in the context of the three-dimensional ultrastructure of the cilium. Here, we apply immuno-SEM to specifically identify proteins on the primary cilia of mouse and human pancreatic islets, including post-translationally modified tubulin, intraflagellar transport (IFT)88, the small GTPase Arl13b, as well as subunits of axonemal dynein. Key parameters in sample preparation, immunolabeling and imaging acquisition are discussed to facilitate similar studies by others in the cilia research community., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

The SPARC tokamak is a high-field, Bt0 ∼12 T, medium-sized, R0 = 1.85 m, tokamak that is presently under construction in Devens, MA, led by Commonwealth Fusion Systems. It will be used to de-risk the high-field tokamak path to a fusion power plant and demonstrate the commercial viability of fusion energy. SPARC's first campaign plan is to achieve Qfus > 1 using an ICRF-heated, <10 MW, high current, Ip ∼ 8.5 MA, L-mode fueled by D-T gas injection, and its second campaign will investigate H-mode operations in D-D. To facilitate plasma control and scientific learning, a targeted set of ∼50 plasma diagnostics are being designed and built for operation during these campaigns. While nearly all diagnostics are based on established techniques, the pace of deployment, relative to the first plasma, and the harshness of the thermal, electromagnetic, and radiation environment are unprecedented for medium-sized tokamaks. An overview of the SPARC diagnostic set is given, providing context to further details communicated by the SPARC team in companion publications that are system-specific. The system engineering philosophy for SPARC diagnostics is outlined, and the design and engineering verification process for components inside and outside the primary vacuum boundary are described. Diagnostics are mounted directly to the vacuum vessel as well as housed within a series of eight midplane and 24 off-midplane replaceable port plugs. With limited exceptions, signal conditioning, digitization electronics and cameras as well as lasers and microwave sources are localized to a series of five Diagnostic Lab spaces, totaling ∼350 m2, located >15 m from the center of the tokamak, on the other side of a 2.4 m concrete shielding wall. A series of 31 large-scale penetrations have been included in the SPARC Tokamak Hall to facilitate integration of early campaign diagnostics and to provide upgradability., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).)

Background: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation., Objectives: The purpose of this study was to develop an AI-ECG algorithm to predict serum potassium level in ESRD patients with smartwatch-generated ECG waveforms., Methods: A cohort of 152,508 patients with 293,557 ECGs paired serum potassium levels obtained within 1 hour at Cedars Sinai Medical Center was used to train an AI-ECG model ("Kardio-Net") to predict serum potassium level. The model was further fine-tuned on 4,337 ECGs from 1,463 patients with ESRD using inputs from 12- and single-lead ECGs. Kardio-Net was evaluated in held-out test cohorts from Cedars Sinai Medical Center and Stanford Healthcare (SHC) as well as a prospective international cohort of 40 ESRD patients with smartwatch ECGs at Chang Gung Memorial Hospital., Results: The Kardio-Net, when applied to 12-lead ECGs, identified severe hyperkalemia (>6.5 mEq/L) with an AUC of 0.852 (95% CI: 0.745-0.956) and a mean absolute error (MAE) of 0.527 mEq/L. In external validation at SHC, the model achieved an AUC of 0.849 (95% CI: 0.823-0.875) and an MAE of 0.599 mEq/L. For single-lead ECGs, Kardio-Net detected severe hyperkalemia with an AUC of 0.876 (95% CI: 0.765-0.987) in the primary cohort and had an MAE of 0.575 mEq/L. In the external SHC validation, the AUC was 0.807 (95% CI: 0.778-0.835) with an MAE of 0.740 mEq/L. Using prospectively obtained smartwatch data, the AUC was 0.831 (95% CI: 0.693-0.975), with an MAE of 0.580 mEq/L., Conclusions: We validate a deep learning model to predict serum potassium levels from both 12-lead ECGs and single-lead smartwatch data, demonstrating its utility for remote monitoring of hyperkalemia., Competing Interests: Funding Support and Author Disclosures Dr Chiu has received funding from the National Science and Technology Council in Taiwan (111-2221-E-182A-008-). Drs Chiu and Ouyang have received funding from Apple and the National Institutes of Health. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

The LLAMA (Lyman Alpha Measurement Apparatus) pinhole camera diagnostic had previously been deployed on DIII-D to measure radial profiles of the Lyman-α (Ly-α) deuterium neutral line brightness across the plasma boundary in the lower chamber to infer neutral deuterium density and ionization rate profiles. This system has recently been upgraded with a new diagnostic head, named ALPACA, that also encloses two pinhole cameras and duplicates the LLAMA views in the upper chamber. Similar to LLAMA, ALPACA provides two times 20 lines of sight, viewing the plasma edge on the inboard and outboard sides with a radial resolution of ∼2.5 cm (FWHM) and an effective time resolution of ∼1 ms that allows for the investigation of inter-ELM dynamics. The extended Ly-α system provides better coverage to study neutrals in experiments with various plasma shapes utilizing both the upper and lower divertors. Furthermore, post-campaign calibration of the LLAMA diagnostic has successfully been demonstrated for the first time. This was facilitated by various upgrades to the calibration set-up and detailed measurements of the emissivity distribution of the Ly-α calibration source using a pinhole collimator. It was found that the sensitivity of the inboard LLAMA pinhole camera was reduced by a factor of 2.0 ± 0.2 over the course of six months of plasma operation in 2021. The upgraded Ly-α system, equipped with improved absolute calibration, will provide key input for neutral fueling and pedestal particle transport studies and for 2D edge transport code validation on the DIII-D tokamak., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

The Multidimensional Behavioral Health Screen (MBHS) is a brief screening measure of behavioral health symptoms. Although the measure was first developed for primary care, it is likely to have clinical utility in other settings. This study examined the MBHS's factor structure and psychometric properties with a university undergraduate and graduate student sample ( n = 602, 58.6% female, 75.9% White, primarily aged 20-24) during the COVID-19 pandemic. MBHS subscale scores demonstrated internal consistency reliability and both convergent and discriminant relations with external, criterion variables. Confirmatory factor analyses supported a 7-subscale factor structure of the MBHS and did not find evidence of higher order factors. Clinical and theoretical implications, as well as future research directions, are discussed., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The primary cilium is a sensory and signaling organelle present on most pancreatic islet endocrine cells, where it receives and interprets a wide range of intra-islet chemical cues including hormones, peptides, and neurotransmitters. The ciliary membrane possesses a molecular composition distinct from the plasma membrane, with enrichment of signaling mediators including G protein-coupled receptors (GPCRs), tyrosine kinase family receptors, membrane transporters and others. When activated, these membrane proteins interact with ion channels and adenylyl cyclases to trigger local Ca 2+ and cAMP activity and transmit signals to the cell body. Here we review evidence supporting the emerging model in which primary cilia on pancreatic islet cells play a central role in the intra-islet communication network and discuss how changes in cilia-mediated paracrine function in islet cells might lead to diabetes., Competing Interests: The authors declare no conflict of interest.

Meta-analysis was used to investigate the potential benefits of stress management interventions (SMIs) on vagally-mediated heart rate variability (HRV) in adults with cardiovascular disease. Electronic bibliographic databases were searched through August 2022. Randomized controlled trials and quasi-experimental studies assessing effects of SMIs on HRV were included. Methodological quality was assessed with a standardized checklist. A pooled effect size was calculated for vagally-mediated HRV indices (standard deviation of normal-to-normal intervals, root mean square of the successive differences, and high frequency power) using random effects models. Fourteen studies (1202 participants, M age : 59 ± 6.25 years; 25% ± 16% women; 61% ± 22% White) were included. Ten studies (11 effects) reported short-term HRV assessment; a small between-group difference emerged for vagally-mediated HRV (d+ = .27, 95% confidence interval [CI] 0.01-0.52, k = 11). Most interventions examined biofeedback; these studies yielded a small between-group difference on vagally-mediated HRV (d+ = 0.31, 95% CI 0.09-0.53, k = 7, Q [6] = 3.82, p = .70, I 2  = 11%). This is the first systematic examination of the effect of SMIs on HRV in adults with CVD. Findings suggest a small effect of SMIs on vagally-mediated HRV, with biofeedback likely driving the effect. More research is required to fully understand whether this benefit on vagally-mediated HRV applies to other SMIs., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Aims: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models., Methods and Results: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites., Conclusion: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret., Competing Interests: Conflict of interest: E.A. reports consulting fees from Apple Inc. M.V.P. reports research funding from NIH/NHLBI and Apple Inc., consulting fees from Apple Inc., Boston Scientific, Biotronik Inc., Bristol Myers Squibb, QALY, Johnson & Johnson, and has an equity interest in QALY., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Previously, we have shown that native ambient mass spectrometry imaging allows the spatial mapping of folded proteins and their complexes in thin tissue sections. Subsequent top-down native ambient mass spectrometry of adjacent tissue section enables protein identification. The challenges associated with protein identification by this approach are (i) the low abundance of proteins in tissue and associated long data acquisition timescales and (ii) irregular spatial distributions which hamper targeted sampling of the relevant tissue location. Here, we demonstrate that these challenges may be overcome through integration of laser capture microdissection in the workflow. We show identification of intact protein assemblies in rat liver tissue and apply the approach to identification of proteins in the granular layer of rat cerebellum., Competing Interests: There are no conflicts of interest., (This journal is © The Royal Society of Chemistry.)

Objective: This study examined the roles of social support and coping self-efficacy (CSE) in attenuating posttraumatic stress (PTS) symptoms during the COVID-19 pandemic among a nonclinical university student sample., Method: Participants ( n = 610; 59% female) completed questionaries assessing psychological distress (Kessler Psychological Distress Scale) at baseline and 6-month follow-up, and social support (Interpersonal Support Evaluation List-12), CSE Scale, and PTS symptoms (Impact of Event Scale-Revised) at 6 months. A path analysis was conducted using SPSS Amos to examine the direct and indirect pathways from psychological distress to PTS symptoms that are accounted for by social support and CSE, controlling for gender., Results: All direct effects in the path analysis were significant except for the relationship between social support and PTS symptoms. Notably, CSE was directly related to PTS symptoms (CSE: β = -.30, p < .001). There was a significant indirect effect of early psychological distress on PTS symptoms 6 months into the pandemic through social support and CSE (β = .14, p < .001)., Conclusions: Individuals with higher levels of social support are more likely to have greater confidence in their coping capabilities, which helps to explain PTS symptom severity after controlling for initial levels of psychological distress and gender. These findings suggest that following a potentially traumatic event, CSE may be one factor to screen for to better identify individuals who are at higher risk for significant psychological difficulties and may benefit from interventions that bolster protective factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied., Methods: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex., Results: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores., Conclusions: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities., Competing Interests: Disclosures Dr Narayan reports research grants from the National Institutes of Health (R01 HL149134 Machine Learning in Atrial Fibrillation and R01 HL83359 Dynamics of Atrial Fibrillation); consulting compensation from Abbott Inc, Up to Date, and LifeSignals.ai; and intellectual property rights from the University of California Regents and Stanford University. E.A. Ashley reports consulting fees from Apple Inc. Dr Perez reports consulting fees from Apple Inc, Boston Scientific, Biotronik Inc, Bristol Myers Squibb, QALY Inc, and Johnson & Johnson and has an equity interest in QALY Inc.

Background: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing., Methods: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems., Findings: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81])., Interpretation: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications., Funding: National Heart, Lung, and Blood Institute., Competing Interests: Declaration of interests DO reports support from the National Institute of Health (NIH; NHLBI R00HL157421) and Alexion, and consulting or honoraria for lectures from EchoIQ, Ultromics, Pfizer, InVision, the Korean Society of Echo, and the Japanese Society of Echo. JWH reports funding from the National Science Foundation (DGE-1656518). PE reports research support from the NIH (T32HL007854-21) and serves on the American College of Cardiology Innovation Council. BC reports consulting from Alnylam, Cardurion, Corvia, CVRX, Cytokinetics, Intellia, and Rocket. TP reports stock in Abbot and Baxter, and research support from the American Heart Association, Eido, Pfizer, Edwards, and the New York Academy of Medicine. JHC reports research funding from NIH/National Institute of Aging (AI17812101), NIH/National Institute on Drug Abuse (UG1DA015815-CTN-0136), AIMI-HAI Partnership, Doris Duke Charitable Foundation (20211260), Google (SPO136094), American Heart Association, and Clinical and Translational Sciences Awards Program by the National Center for Advancing Translational Sciences (R56LM013365); is a co-founder of Reaction Explorer; and receives consulting fees from Sutton Pierce and Younker Hyde MacFarlane. MP reports research support from the NIH (HL136390), patents for ECG-based cardiac arrhythmia detection (WO2014205310A3), and consulting fees from Apple, Biotronik, Boston Scientific, QALY, Johnson and Johnson, and Bristol Myers Squibb. CMA reports support from NIH (HL165840), consulting fees from Medtronic, Novartis, Illumina, and Medtronic, and participation in advisory boards for Boston Scientific, Medtronic, and Element Science; and is a trustee on the board for the Heart Rhythm Society. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND license. Published by Elsevier Ltd.. All rights reserved.)

Somatostatin is a paracrine modulator of insulin secretion and beta cell function with pleotropic effects on glucose homeostasis. The mechanism of somatostatin-mediated communication between delta and beta cells is not well-understood, which we address in this study via the ciliary somatostatin receptor 3 (SSTR3). Primary cilia are membrane organelles that act as signaling hubs in islets by virtue of their subcellular location and enrichment in signaling proteins such as G-protein coupled receptors (GPCRs). We show that SSTR3, a ciliary GPCR, mediates somatostatin suppression of insulin secretion in mouse islets. Quantitative analysis of calcium flux using a mouse model of genetically encoded beta cell-specific GCaMP6f calcium reporter shows that somatostatin signaling alters beta cell calcium flux after physiologic glucose stimulation, an effect that depends on endogenous SSTR3 expression and the presence of intact primary cilia on beta cells. Comparative in vitro studies using SSTR isoform antagonists demonstrate a role for SSTR3 in mediating somatostatin regulation of insulin secretion in mouse islets. Our findings support a model in which ciliary SSTR3 mediates a distinct pathway of delta-to-beta cell regulatory crosstalk and may serve as a target for paracrine modulation.