Dean Nachman

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) commonly coexists with heart failure with preserved ejection fraction (HFpEF), yet the mechanisms linking hepatic steatosis to diastolic dysfunction remain unclear. Methods: A combined murine model of pressure overload using transverse aortic constriction (TAC) and diet-induced MASLD was developed to investigate the liver-heart interaction in HFpEF. Cardiac function and structure were assessed by echocardiography and histopathology. Hepatic transcriptomics and cardiac metabolomics were integrated to identify molecular pathways underlying cardiac remodeling and diastolic dysfunction. Results: While left ventricular ejection fraction remained preserved, HFpEF-MASLD mice exhibited significantly worse diastolic function (lower septal è velocity and higher E/è ratio), greater left ventricular hypertrophy, and more extensive myocardial fibrosis compared with HFpEF alone. Immunofluorescence demonstrated augmented myocardial inflammation with increased CD3 ⁺ T-cell and CD68 ⁺ macrophage infiltration in the combined HFpEF-MASLD group. Transcriptomic analysis demonstrated marked down-regulation of genes implicated in retinoic acid signaling, confirmed by reduced expression of retinoic acid receptors (RARα, RARβ) and retinol dehydrogenases (RDH) in both hepatic and cardiac tissues. Cardiac metabolomics revealed suppression of arginine biosynthesis, the obligate substrate for endothelial nitric oxide (NO) synthase, suggesting a potential link to reduced NO-mediated vascular and myocardial signaling. Conclusions: MASLD aggravates HFpEF through converging inflammatory and metabolic derangements. Disruption of retinoic acid and arginine-NO pathways may represent an important mechanistic link between hepatic steatosis and diastolic dysfunction that warrants further mechanistic and translational investigation.

Introduction: Direct assessment of organ perfusion during hemorrhage is essential for effective interventions. Initial blood loss triggers compensatory mechanisms to preserve vital organ perfusion, but failure leads to decreased perfusion and injury. Vital signs provide indirect measures of cerebral health, while diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS) enable continuous, noninvasive monitoring of cerebral microvascular blood flow (CBF), cerebral blood volume (CBV), and oxygenation, offering real-time insights for detection and intervention. Materials and Methods: Adult swine models of controlled (30% blood volume reduction via syringe) and uncontrolled (partial hepatectomy) hemorrhage were studied. Systemic measures, i.e., blood pressure (BP), end-tidal CO2 (EtCO2), and heart rate, were compared with cerebral measures (CBF, CBV, and oxygenation) assessed by DCS-NIRS. Monitoring spanned 3 hours post-intervention, with periodic laboratory tests for injury confirmation. Results: Systemic and cerebral hemodynamics were assessed pre-intervention, post-intervention, and during follow-up, revealing significant differences across timepoints for BP and cerebral measures (P < .05). BP, CBF, and CBV decreased post-intervention in both cohorts, with recovery during follow-up only in the controlled cohort, where relative blood flow index (rBFI), total hemoglobin (HbT), and oxygenated hemoglobin (HbO) showed large reductions post-blood loss (large positive effects) and partial recovery during follow-up (large negative effects); however, rBFI and HbO remained below baseline, indicating incomplete recovery of cerebral perfusion and oxygenation. In the uncontrolled cohort, rBFI, HbT, and HbO decreased significantly post-injury, with reductions persisting below baseline during follow-up, reflecting impaired cerebral perfusion and oxygenation. EtCO2 remained stable in the controlled cohort (χ2(2) = 3.8, P = .15) but varied significantly in the uncontrolled cohort (χ2(2) = 9.25, P < .01). Lactic acid levels differed significantly in both cohorts, while pH changes were significant only in uncontrolled hemorrhage. Conclusions: DCS-NIRS-derived biomarkers for cerebral hemodynamics reflect changes observed in BP in controlled and uncontrolled hemorrhage, thus supporting our previous findings of DCS-NIRS’s ability to detect hemorrhagic shock.

Intradialytic hypotension (IDH) is a severe complication of hemodialysis (HD) with a significant impact on morbidity and mortality. In this study, we used a wearable device for the continuous monitoring of hemodynamic vitals to detect hemodynamic changes during HD and attempted to identify IDH. End-stage kidney disease patients were continuously monitored 15 min before starting the session and until 15 min after completion of the session, measuring heart rate (HR), noninvasive cuffless systolic and diastolic blood pressure (SBP and DBP), stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR). Data were analyzed retrospectively and included comparing BP measured by the wearable devices (recorded continuously every 5 s) and the cuff-based devices. A total of 98 dialysis sessions were included in the final analysis, and IDH was identified in 22 sessions (22.5%). Both SBP and DBP were highly correlated (r > 0.62, p < 0.001 for all) between the wearable device and the cuff-based measurements. Based on the continuous monitoring, patients with IDH had earlier and more profound reductions in SBP and DBP during the HD treatment. In addition, nearly all of the advanced vitals differed between groups. Further studies should be conducted in order to fully understand the potential of noninvasive advanced continuous monitoring in the prediction and prevention of IDH events.

There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their responses. In this study, 29 HF patients were monitored during outpatient intravenous diuresis, using a noninvasive wearable multi-parameter monitor. Analysis of changes in these parameters during the course of diuresis aimed to recognize subgroups with different response patterns. Parameters did not change significantly, however, subgroup analysis of the last quartile of treatment showed significant differences in cardiac output, cardiac index, stroke volume, pulse rate, and systemic vascular resistance according to gender, and in systolic blood pressure according to habitus. Changes in the last quartile could be differentiated using k-means, a technique of unsupervised machine learning. Moreover, patients’ responses could be best clustered into four groups. Analysis of baseline parameters showed that two of the clusters differed by baseline parameters, body mass index, and diabetes status. To conclude, we show that physiological changes during diuresis in HF patients can be categorized into subgroups sharing similar response trends, making noninvasive monitoring a potential key to personalized treatment in HF.

Recent technological developments enable non-invasive monitoring of advanced hemodynamic parameters, allowing big data-driven insights on diurnal changes for the first time. This study aimed to describe the trends of multiple advanced cardiovascular parameters among ambulatory individuals using a medical grade wearable monitor. Common and advanced cardio-respiratory parameters were monitored for 24 hours in ambulatory volunteers using a photoplethysmography-based wearable monitor. The multi vital ambulatory monitoring (MVAM) included stroke volume (SV), cardiac index (CI), systemic vascular resistance (SVR), blood oxygen saturation (SpO2), respiratory rate (RR), systolic (SBP) and diastolic blood pressure (DBP), mean arterial pressure (MAP), pulse pressure (PP), and heart rate (HR). Diurnal variations were evaluated among all participants, and analyses were stratified based on sex, age, and body-mass index (BMI). A total of 256 participants were included in the analysis. All measured parameters showed significant (p30 kg/m²) had a higher SBP and DBP, and lower SV and CI. SVR was higher among the elderly. Diurnal changes using MVAM corresponding with awake-sleep hours were noticed in the monitored parameters and differed between sex, age, and BMI groups. This suggests that wearable monitoring platforms could shed more light on hemodynamic changes in subgroups, and might help with future efforts to provide tailored personalized medicine and pre-symptomatic diagnosis and prevention. Type of funding sources: None. Figure 1. 24-hour changes in hemodynamics Figure 2. Stratification based on sex age and BMI

Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.

COVID-19 exerts deleterious cardiopulmonary effects, leading to a worse prognosis in the most affected. This retrospective multi-center observational cohort study aimed to analyze the trajectories of key vitals amongst hospitalized COVID-19 patients using a chest-patch wearable providing continuous remote patient monitoring of numerous vital signs. The study was conducted in five COVID-19 isolation units. A total of 492 COVID-19 patients were included in the final analysis. Physiological parameters were measured every 15 min. More than 3 million measurements were collected including heart rate, systolic and diastolic blood pressure, cardiac output, cardiac index, systemic vascular resistance, respiratory rate, blood oxygen saturation, and body temperature. Cardiovascular deterioration appeared early after admission and in parallel with changes in the respiratory parameters, showing a significant difference in trajectories within sub-populations at high risk. Early detection of cardiovascular deterioration of COVID-19 patients is achievable when using frequent remote patient monitoring.

Hemorrhagic shock is the number one cause of death on the battlefield and in civilian trauma as well. Mathematical modeling has been applied in this context for decades; however, the formulation of a satisfactory model that is both practical and effective has yet to be achieved. This paper introduces an upgraded version of the 2007 Zenker model for hemorrhagic shock termed the ZenCur model that allows for a better description of the time course of relevant observations. Our study provides a simple but realistic mathematical description of cardiovascular dynamics that may be useful in the assessment and prognosis of hemorrhagic shock. This model is capable of replicating the changes in mean arterial pressure, heart rate, and cardiac output after the onset of bleeding (as observed in four experimental laboratory animals) and achieves a reasonable compromise between an overly detailed depiction of relevant mechanisms, on the one hand, and model simplicity, on the other. The former would require considerable simulations and entail burdensome interpretations. From a clinical standpoint, the goals of the new model are to predict survival and optimize the timing of therapy, in both civilian and military scenarios.

Rupture of Abdominal aortic aneurysm (AAA) is among the 15 leading causes of death after age 65. Using high frequency ultrasound, we showed that photobiomodulation (PBM) prevents formation and progression of AAA in the angiotensin‐II (Ang‐II)‐infused, apolipoprotein‐e‐deficient mouse model. In the current study we report that while challenge of porcine aortic Smooth Muscle Cells (SMCs) with Ang‐II (1 μM) resulted in a marked decay in mitochondrial membrane potential (MitMP) vs non‐challenged cells, treatment with PBM (continuous diode laser, 780 nm, 6.7 mW/cm², 5 minutes, 2 J/cm²) or pre‐incubation with estrogen (50 nM, 1 hour) significantly attenuated this deterioration in MitMP. We also report that PBM and estrogen markedly affected porcine aortic SMC contraction and modified mitochondrial dispersion reflecting important influence on SMC function. These studies provide strong evidence of the important underlying role of mitochondria in the preventive effect of PBM on formation and progression of AAA and its reduced incidence and delayed onset in women.