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BackgroundThe traditional classification of left ventricular hypertrophy (LVH), which relies on left ventricular geometry, fails to correlate with outcomes among patients with increased LV mass (LVM).ObjectivesTo identify unique clinical phenotypes of increased LVM patients using unsupervised cluster analysis, and to explore their association with clinical outcomes.MethodsAmong the UK Biobank participants, increased LVM was defined as LVM index ≥72 g/m2 for men, and LVM index ≥55 g/m2 for women. Baseline demographic, clinical, and laboratory data were collected from the database. Using Ward's minimum variance method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied.ResultsIncreased LVM was found in 4,255 individuals, with an average age of 64 ± 7 years. Of these patients, 2,447 (58%) were women. Through cluster analysis, four distinct subgroups were identified. Over a median follow-up period of 5 years (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were admissioned due to HF, 29 (0.7%) were admissioned due to VA, and 208 (5%) were admissioned due to AF. Univariate Cox analysis demonstrated significantly elevated risks of major events for patients in the 2nd (HR = 1.6; 95% CI 1.2–2.16; p

Background and Aims: Metabolic dysfunction-associated steatohepatitis is an advanced form of nonalcoholic fatty liver disease and a leading cause of end-stage liver disease and transplantation. Insulin resistance and inflammation underlie the pathogenesis of the disease. Methods: This double-blind, randomized, placebo-controlled, multicenter feasibility clinical trial aimed to determine the safety of oral 8 mg insulin in patients with metabolic dysfunction-associated steatohepatitis and type 2 diabetes mellitus. Patients were treated twice daily for 12 weeks with an 8 mg insulin (n = 21) or placebo (n = 11) capsule. Safety was monitored throughout the study. MRI-proton density fat fraction assessed liver fat content, and Fibroscan® measured liver fibrosis and steatosis levels at screening and after 12 weeks of treatment. Results: No severe drug-related adverse events were reported during the study. After 12 weeks of treatment, mean percent reductions in whole-liver (−11.2% vs −6.5%, respectively) and liver segment 3 (−11.7% vs +0.1%, respectively) fat content was higher in the insulin than in the placebo arm. Patients receiving insulin showed a median −1.2 kPa and −21.0 dB/m reduction from baseline fibrosis and steatosis levels, respectively, while placebo-treated patients showed median increases of 0.3 kPa and 13.0 dB/m, respectively. At Week 12, oral insulin was associated with a mean of 0.27% reduction and placebo with a 0.23% increase from baseline hemoglobin A1c levels. Mean percent changes from baseline alanine aminotransferase, and aspartate aminotransferase levels were −10% and −0.8%, respectively, in the oral insulin and 3.0% and 13.4%, in the placebo arm. Conclusion: The results of this feasibility study support the safety and potential therapeutic effect of orally delivered insulin on liver fibrosis, fat accumulation, and inflammatory processes (NIH Clinical Trials No. NCT04618744).

AbstractAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents. Numerous methods are used to overcome this problem with moderate success. Besides efforts of antimicrobial stewards, several artificial intelligence (AI)-based technologies are being explored for preventing resistance development. These first-generation systems mainly focus on improving patients’ adherence. Chronobiology is inherent in all biological systems. Host response to infections and pathogens activity are assumed to be affected by the circadian clock. This paper describes the problem of antimicrobial resistance and reviews some of the current AI technologies. We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. An algorithm-controlled regimen that improves the long-term effectiveness of antimicrobial agents is being developed based on the implementation of variability in dosing and drug administration times. The method provides a means for ensuring a sustainable response and improved outcomes. Ongoing clinical trials determine the effectiveness of this second-generation system in chronic infections. Data from these studies are expected to shed light on a new aspect of resistance mechanisms and suggest methods for overcoming them.IMPORTANCE SECTIONThe paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.Key messagesAntimicrobial resistance results from the widespread use of antimicrobial agents and is a significant obstacle to the effectiveness of these agents.We present the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance.

Background Recent technological developments enable big data-driven insights on diurnal changes. This study aimed to describe the trajectory of multiple and advanced parameters using a medical-grade wearable remote patient monitor. Methods Parameters were monitored for 24 h in 256 ambulatory participants who kept living their normal life. Parameters included heart rate, blood pressure, stroke volume, cardiac index, systemic vascular resistance, blood oxygen saturation, and respiratory rate. Diurnal variations were evaluated, and analyses were stratified based on sex, age, and body mass index. Results All parameters showed diurnal changes ( p

Coronaviruses are the causative agents of several recent outbreaks, including the COVID-19 pandemic. One therapeutic approach is blocking viral binding to the host receptor. As binding largely depends on electrostatic interactions, we hypothesized possible inhibition of viral infection through application of electric fields, and tested the effectiveness of Tumor Treating Fields (TTFields), a clinically approved cancer treatment based on delivery of electric fields. In preclinical models, TTFields were found to inhibit coronavirus infection and replication, leading to lower viral secretion and higher cell survival, and to formation of progeny virions with lower infectivity, overall demonstrating antiviral activity. In a pilot clinical study (NCT04953234), TTFields therapy was safe for patients with severe COVID-19, also demonstrating preliminary effectiveness data, that correlated with higher device usage.

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.

BackgroundEvidence regarding the mortality benefit of implantable cardioverter defibrillator (ICD) non-ischemic dilated cardiomyopathy (NIDCM) is inconsistent. The most recent randomized study, the DANISH trial, did not find improved outcomes with ICD. However, based on previous studies and meta-analyses, current guidelines still highly recommend ICD implantation in NIDCM patients. The introduction of novel medications for heart failure improved the clinical outcome dramatically. We aimed in this study to evaluate the effect of Angiotensin Receptor-Neprilysin Inhibitors (ARNi) and sodium-glucose transport protein 2 inhibitors (SGLT2i) on the mortality benefit of ICD in NIDCM.MethodsWe used a previous metanalysis algorithm and added an updated comprehensive literature search in PubMed for randomized control trials that examined the mortality benefit of ICD in NIDCM vs. optimal medical treatment. The primary outcome included death from any cause. We did a meta-regression analysis to search for a single independent factor affecting mortality. Using previous data, we evaluated the theoretical effect of ICD implementation on patients treated with SGLT2 inhibitors and ARNi.ResultsNo new articles were added to the results of the previous meta-analysis. 2,622 patients with NIDCM from 5 cohort studies published between 2002 and 2016 were included in the analysis. 50% of them underwent ICD implantation for primary prevention of sudden cardiac death, and 50% did not. ICD was associated with a significantly decreased risk for death from any cause compared to control (OR = 0.79, 95%CI: 0.66–0.95, p = 0.01, I2 = 0%). The theoretical addition of ARNi and the SGLT2 inhibitor dapagliflozin did not change the significant mortality effect of ICD (OR = 0.82, 95%CI: 0.7–0.9, p = 0.001, I2 = 0%) and (OR = 0.82, 95%CI: 0.7–0.9, p = 0.001, I2 = 0%). A meta-regression revealed no association between death from any cause and left bundle branch block (LBBB), use of amiodarone, use of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers, year initiated enrollment, and the year ended enrollment (R2 = 0.0).ConclusionIn patients with NIDCM, the addition of ARNi and SGLT2i did not affect the mortality advantages of ICD for primary prevention.PROSPERO registry numberhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42023403210.

Abstract We present a case of bacterial endocarditis with both methicillin-sensitive and methicillin-resistant Staphylococcus aureus, which based on typing, originated from two distinct clones. Such a case may be misinterpreted by microbiology lab automation to be a monoclonal multi-drug resistant Staphylococcus aureus, while simple microbiology techniques will instantly reveal distinct clonality.

Heart failure is a major public health problem, which is associated with significant mortality, morbidity, and healthcare expenditures. A substantial amount of the morbidity is attributed to volume overload, for which loop diuretics are a mandatory treatment. However, the variability in response to diuretics and development of diuretic resistance adversely affect the clinical outcomes. Morevoer, there exists a marked intra- and inter-patient variability in response to diuretics that affects the clinical course and related adverse outcomes. In the present article, we review the mechanisms underlying the development of diuretic resistance. The role of the autonomic nervous system and chronobiology in the pathogenesis of congestive heart failure and response to therapy are also discussed. Establishing a novel model for overcoming diuretic resistance is presented based on a patient-tailored variability and chronotherapy-guided machine learning algorithm that comprises clinical, laboratory, and sensor-derived inputs, including inputs from pulmonary artery measurements. Inter- and intra-patient signatures of variabilities, alterations of biological clock, and autonomic nervous system responses are embedded into the algorithm; thus, it may enable a tailored dose regimen in a continuous manner that accommodates the highly dynamic complex system.

Heart failure with reduced ejection fraction (HFrEF) is an increasing global pandemic affecting more than 30 million individuals worldwide. Importantly, HFrEF is frequently accompanied by the presence of cardiac and non-cardiac comorbidities that may greatly influence the management and prognosis of the disease. In this review article, we will focus on three important comorbidities in HFrEF; atrial fibrillation (AF), advanced renal disease, and elderly, which all have a paramount impact on progression of the disease, management strategies, and response to therapy. AF is very common in HFrEF and shares many risk factors. AF aggravates heart failure and contributes to HF-related adverse clinical outcomes; hence it requires special consideration in HFrEF management. The kidney function is largely affected by the reduced cardiac output developed in the setting of HFrEF, and the neurohormonal feedback effects create a complex interplay that pose challenges in the management of HFrEF when renal function is significantly impaired. Cardiorenal syndrome is a challenging sequela with increased morbidity and mortality thereby reflecting the delicate and complex balance between the heart and the kidney in HFrEF and renal failure conditions. Furthermore, patients with advanced renal failure have poor prognosis in the presence of HFrEF with limited treatment options. Finally, aging and frailty are important factors that influence treatment strategies in HFrEF with greater emphasis on tolerability and safety of the various HFrEF therapies in elderly individuals.