Adropin is a peptide hormone, which plays a role in energy homeostasis and controls glucose and fatty acid metabolism. Its levels correlate with changes in carbohydratelipid metabolism, metabolic diseases, central nervous system function, endothelial function and cardiovascular disease. Both metabolic pathways and adropin are regulated by the circadian clocks. Here, we review the roles of the autonomic nervous system and circadian rhythms in regulating metabolic pathways and energy homeostasis. The beneficial effects of chronotherapy in various systems are discussed. We suggest a potential role for adropin as a mediator of the metabolic system-autonomic nervous system axis. We discuss the possibility of establishing an individualized adropin and circadian rhythm-based platform for implementing chronotherapy, and variability signatures for improving the efficacy of adropin-based therapies are discussed. K E Y W O R D S adropin, autonomic nervous system, chronobiology, metabolic syndrome 1 | INTRODUCTION Metabolic syndrome (MetS) is a cluster of metabolic and cardiovascular complications, which increase the risk of cardiovascular diseases (CVD) and type 2 diabetes (T2D). 1 The pathogenesis of MetS involves multiple factors, including the autonomic nervous system (ANS) and the circadian rhythm. 2 Recently, MetS was also shown to increase the risk and mortality of cancer, 3 with which it shares many risk factors including age, genetic factors, obesity, physical inactivity, unhealthy diet, alcohol, smoking, endocrine disruptors exposure and air pollution. Recent studies have suggested that several peptide hormones play important roles in the modulation of systemic metabolism and energy homeostasis. Adropin is a product of the energy homeostasis associated (Enho) gene and a peptide hormone that plays a role in the regulation of energy homeostasis and controls glucose and fatty acid metabolism. It was originally described as a secreted peptide, with

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.

Hepatic encephalopathy (HE) is a common, incapacitating disease that affects many patients with cirrhosis. While several therapies have proven effective in the treatment and prevention of this condition, several patients continue to suffer from covert disease or episodes of relapse. The circadian rhythm has been demonstrated to be pivotal for many body functions, including those of the liver. Here, we explore the impact of circadian rhythm-dependent signaling on the liver and discuss the evidence of its impact on liver pathology and metabolism. We describe the various pathways through which circadian influences are mediated. Finally, we introduce a novel method for improving patient response to drugs aimed at treating HE by utilizing the circadian rhythm. A digital system that introduces a customization-based technology for improving the response to therapies is presented as a hypothetical approach for improving the effectiveness of current medications used for the treatment of recurrent and persistent hepatic encephalopathy.

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 HFrelated 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.

Antimicrobial 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 SECTION The paper presents the establishment of a second-generation AI chronobiology-based approach to help in preventing further resistance and possibly overcome existing resistance. Key messages Antimicrobial 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.

No abstract

Despite significant advances in the management of heart failure (HF), further improvement in the outcome of this chronic and progressive disease is still considered a major unmet need. Recurrent hospitalizations due to decompensated HF frequently occur, resulting in increased morbidity and mortality rates. Past attempts at early detection of clinical deterioration were mainly based on monitoring of signs and symptoms of HF exacerbation, which have mostly given disappointing results. Extensive research of the pathophysiology of HF decompensation has indicated that hemodynamic alterations start days prior to clinical manifestation. Novel technologies aim to monitor these minute hemodynamic changes, allowing time for therapeutic interventions to prevent hemodynamic derangement and HF exacerbation. The latest noticeable advancements include assessment of lung fluid volume, wearable devices with integrated sensors, and microelectromechanical systems-based implantable devices for continuous measurement of cardiac filling pressures. This manuscript will review the rationale for monitoring HF patients and discuss previous and ongoing attempts to develop clinically meaningful monitoring devices to improve daily HF health care, with particular emphasis on the recent advances and clinical trials relevant to this evolving field.

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.