Randomness in microtubule dynamics: an error that requires correction or an inherent plasticity required for normal cellular function?

Correlations between components of the immune system

Background

No evidence of the possibility of altering a constituent of the immune system without directly affecting one of its associated components has yet been shown.

Methods

A schematic model was developed in which two triggers, fasting and splenectomy, were studied for their ability to affect the expression of cell membrane epitopes and the cytokine secretion of out-of-body autogeneic and syngeneic lymphocytes.

Results

The effect of fasting and/or splenectomy on promoting correlations between immune systems was studied by determining the alterations in expressions of cell membrane epitopes and in cytokine secretion by out-of-body autogeneic and syngeneic lymphocytes. The effect of fasting as a trigger decreased expression of CD8 and CD25 and increased TNFα levels. The effect of splenectomy as a trigger was investigated in non-fasting mice by comparing splenectomized and non-splenectomized mice. An increase in the CD8 expression and in TNFα, IFNg, and IL10 secretion was noted. The effect of splenectomy as a trigger in fasting mice was determined by comparing splenectomized and non-splenectomized mice. Splenectomy significantly affected the expression of CD25 and CD4 CD25 and on secretion of TNFα, IFNg, and IL10. To determine the effect of keeping the cells in an out-of-body location on the expression of lymphocyte epitopes, tubes kept on top of the cages of the fasting mice were compared with tubes kept on top of empty cages. The results showed a significant change in the CD8 expression was noted. To determine the effect of keeping cells in an out-of-body location on cytokine secretion, tubes kept on cages were tested for cytokine levels significant decrease was noted in the secretion of TNFα and IFNg.

Conclusions

The study showed that a mouse could affect cells at a distance and alter the expression of surface markers and cytokine secretion following two types of triggers: fasting and/or splenectomy. The data characterized a system for the induction of correlations between two's immune system components without a transfer of mediators. It suggests that an out-of-body correlation can be induced between two components of the immune system.

Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system

Objective: Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. We aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. Methods: In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, long-term memory, and verbal fluency. Results: The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stable vs. unstable PA ; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stable vs. unstable PA ; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stable vs. unstable PA ; P=0.02; adjusted P=0.04) and improved short-term memory (score change: -0.28 vs. +0.29, stable vs. unstable PA ; P=0.05). Conclusion: Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.

Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections

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 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.

The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation

Variability characterizes breathing, cellular respiration, and the underlying quantum effects. Variability serves as a mechanism for coping with changing environments; however, this hypothesis does not explain why many of the variable phenomena of respiration manifest randomness. According to the constrained disorder principle (CDP), living organisms are defined by their inherent disorder bounded by variable boundaries. The present paper describes the mechanisms of breathing and cellular respiration, focusing on their inherent variability. It defines how the CDP accounts for the variability and randomness in breathing and respiration. It also provides a scheme for the potential role of respiration variability in the energy balance in biological systems. The paper describes the option of using CDP-based artificial intelligence platforms to augment the respiratory process's efficiency, correct malfunctions, and treat disorders associated with the respiratory system.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems

Digital twins are computer programs that use real-world data to create simulations that predict the performance of processes, products, and systems. Digital twins may integrate artificial intelligence to improve their outputs. Models for dealing with uncertainties and noise are used to improve the accuracy of digital twins. Most currently used systems aim to reduce noise to improve their outputs. Nevertheless, biological systems are characterized by inherent variability, which is necessary for their proper function. The constrained-disorder principle defines living systems as having a disorder as part of their existence and proper operation while kept within dynamic boundaries. In the present paper, we review the role of noise in complex systems and its use in bioengineering. We describe the use of digital twins for medical applications and current methods for dealing with noise and uncertainties in modeling. The paper presents methods to improve the accuracy and effectiveness of digital twin systems by continuously implementing variability signatures while simultaneously reducing unwanted noise in their inputs and outputs. Accounting for the noisy internal and external environments of complex biological systems is necessary for the future design of improved, more accurate digital twins.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial

INTRODUCTION

Diuretics are a mainstay therapy for congestive heart failure (CHF); however, over one-third of patients develop diuretic resistance. Second-generation artificial intelligence (AI) systems introduce variability into treatment regimens to overcome the compensatory mechanisms underlying the loss of effectiveness of diuretics. This open-labeled, proof-of-concept clinical trial sought to investigate the ability to improve diuretic resistance by implementing algorithm-controlled therapeutic regimens.

METHODS

Ten CHF patients with diuretic resistance were enrolled in an open-labeled trial where the Altus Care™ app managed diuretics' dosage and administration times. The app provides a personalized therapeutic regimen creating variability in dosages and administration times within pre-defined ranges. Response to therapy was measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ) score, 6-minute walk test (SMW), N-terminal pro-brain natriuretic peptide (NT-proBNP) levels, and renal function.

RESULTS

The second-generation, AI-based, personalized regimen alleviated diuretic resistance. All evaluable patients demonstrated clinical improvement within ten weeks of intervention. A dose reduction (based on a three-week average before and last three weeks of intervention) was achieved in 7/10 patients (70 %, p = 0.042). The KCCQ score improved in 9/10 (90 %, p = 0.002), the SMW improved in 9/9 (100 %, p = 0.006), NT-proBNP was decreased in 7/10 (70 %, p = 0.02), and serum creatinine was decreased in 6/10 (60 %, p = 0.05). The intervention was associated with reduced number of emergency room visits and the number of CHF-associated hospitalizations.

SUMMARY

The results support that the randomization of diuretic regimens guided by a second-generation personalized AI algorithm improves the response to diuretic therapy. Prospective controlled studies are needed to confirm these findings.

Making use of noise in biological systems

Disorder and noise are inherent in biological systems. They are required to provide systems with the advantages required for proper functioning. Noise is a part of the flexibility and plasticity of biological systems. It provides systems with increased routes, improves information transfer, and assists in response triggers. This paper reviews recent studies on noise at the genome, cellular, and whole organ levels. We focus on the need to use noise in system engineering. We present some of the challenges faced in studying noise. Optimizing the efficiency of complex systems requires a degree of variability in their functions within certain limits. Constrained noise can be considered a method for improving system robustness by regulating noise levels in continuously dynamic settings. The digital pill-based artificial intelligence (AI)-based platform is the first to implement second-generation AI comprising variability-based signatures. This platform enhances the efficacy of the therapeutic regimens. Systems requiring variability and mechanisms regulating noise are mandatory for understanding biological functions.

Microtubules as a potential platform for energy transfer in biological systems: a target for implementing individualized, dynamic variability patterns to improve organ function

Variability characterizes the complexity of biological systems and is essential for their function. Microtubules (MTs) play a role in structural integrity, cell motility, material transport, and force generation during mitosis, and dynamic instability exemplifies the variability in the proper function of MTs. MTs are a platform for energy transfer in cells. The dynamic instability of MTs manifests itself by the coexistence of growth and shortening, or polymerization and depolymerization. It results from a balance between attractive and repulsive forces between tubulin dimers. The paper reviews the current data on MTs and their potential roles as energy-transfer cellular structures and presents how variability can improve the function of biological systems in an individualized manner. The paper presents the option for targeting MTs to trigger dynamic improvement in cell plasticity, regulate energy transfer, and possibly control quantum effects in biological systems. The described system quantifies MT-dependent variability patterns combined with additional personalized signatures to improve organ function in a subject-tailored manner. The platform can regulate the use of MT-targeting drugs to improve the response to chronic therapies. Ongoing trials test the effects of this platform on various disorders.

Low-Dose Colchicine Attenuates Sepsis-Induced Liver Injury: A Novel Method for Alleviating Systemic Inflammation

Sepsis is a significant public health challenge. The immune system underlies the pathogenesis of the disease. The liver is both an active player and a target organ in sepsis. Targeting the gut immune system using low-dose colchicine is an attractive method for alleviating systemic inflammation in sepsis without inducing immunosuppression. The present study aimed to determine the use of low-dose colchicine in LPS-induced sepsis in mice. C67B mice were injected intraperitoneal with LPS to induce sepsis. The treatment group received 0.02 mg/kg colchicine daily by gavage. Short and extended models were performed, lasting 3 and 5 days, respectively. We followed the mice for biochemical markers of end-organ injury, blood counts, cytokine levels, and liver pathology and conducted proteomic studies on liver samples. Targeting the gut immune system using low-dose colchicine improved mice's well-being measured by the murine sepsis score. Treatment alleviated the liver injury in septic mice, manifested by a significant decrease in their liver enzyme levels, including ALT, AST, and LDH. Treatment exerted a trend to reduce creatinine levels. Low-dose colchicine improved liver pathology, reduced inflammation, and reduced the pro-inflammatory cytokine TNFα and IL1-β levels. A liver proteomic analysis revealed low-dose colchicine down-regulated sepsis-related proteins, alpha-1 antitrypsin, and serine dehydratase. Targeting the gut immune system using low-dose colchicine attenuated liver injury in LPS-induced sepsis, reducing the pro-inflammatory cytokine levels. Low-dose colchicine provides a safe method for immunomodulation for multiple inflammatory disorders.

Department of Medicine 2040: Implementing a Constrained Disorder Principle-Based Second-Generation Artificial Intelligence System for Improved Patient Outcomes in the Department of Internal Medicine

Internal medicine departments must adapt their structures and methods of operation to accommodate changing healthcare systems. The present paper discusses some challenges departments of medicine face as healthcare providers and consumers continue to change. A co-pilot model is described in this article for augmenting physicians rather than replacing them. The paper presents the co-pilot models to improve diagnoses, treatments, and monitoring. Personalized variability patterns based on the constrained-disorder principle (CDP) are described to assess chronic therapies' effectiveness in improving patient outcomes. Based on CDP-based enhanced digital twins, this paper presents personalized treatments and follow-ups that improve diagnosis accuracy and therapy outcomes. While maintaining their professional values, departments of internal medicine must respond proactively to the needs of patients and healthcare systems. To meet the needs of patients and healthcare systems, they must strive for medical professionalism and adapt to the dynamic environment.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

Aging is a complex biological process with multifactorial nature underlined by genetic, environmental, and social factors. In the present paper, we review several mechanisms of aging and the pre-clinically and clinically studied anti-aging therapies. Variability characterizes biological processes from the genome to cellular organelles, biochemical processes, and whole organs' function. Aging is associated with alterations in the degrees of variability and complexity of systems. The constrained disorder principle defines living organisms based on their inherent disorder within arbitrary boundaries and defines aging as having a lower variability or moving outside the boundaries of variability. We focus on associations between variability and hallmarks of aging and discuss the roles of disorder and variability of systems in the pathogenesis of aging. The paper presents the concept of implementing the constrained disease principle-based second-generation artificial intelligence systems for improving anti-aging modalities. The platform uses constrained noise to enhance systems' efficiency and slow the aging process. Described is the potential use of second-generation artificial intelligence systems in patients with chronic disease and its implications for the aged population.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Personalized-Inherent Variability in a Time-Dependent Immune Response: A Look into the Fifth Dimension in Biology

Introduction

Individualized response to the immune triggers influences the course of immune-mediated diseases and the response to immunotherapies. Both inter- and intra-subject variations occur in time-dependent dynamics of biological systems. The present study aimed to establish a model for inherent personalized-time-dependent variability in response to immune triggers.

Methods

Male C57BL/6 mice were administered concanavalin A (ConA) and followed every 2 h for 10 h and at 24 h for serum alanine aminotransferase (ALT) levels.

Results

A marked intragroup variability was noted for both the timing of the effect of ConA, the magnitude of the increase in ALT levels, and the time to peak. While in some mice, a peak level was achieved, whereas a continuous increase in liver damage was noted in others. Four mice died at different time points during the study irrespective of their liver damage, further supporting the individualized-based response to the trigger.

Conclusions

This feasibility study established a model for determining the personalized-inherent variability in a time-dependent response to the immune triggers. These results highlight the importance of considering both the time and the wide range of individualized variability in immune responses while designing personalized-based immunotherapies.

The role of bacterial translocation in sepsis: a new target for therapy

Sepsis is a leading cause of death in critically ill patients, primarily due to multiple organ failures. It is associated with a systemic inflammatory response that plays a role in the pathogenesis of the disease. Intestinal barrier dysfunction and bacterial translocation (BT) play pivotal roles in the pathogenesis of sepsis and associated organ failure. In this review, we describe recent advances in understanding the mechanisms by which the gut microbiome and BT contribute to the pathogenesis of sepsis. We also discuss several potential treatment modalities that target the microbiome as therapeutic tools for patients with sepsis.

Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects

Opioids remain an essential part of the treatment of chronic pain. However, their use and increasing rates of misuse are associated with high morbidity and mortality. The development of tolerance to opioids and analgesics further complicates dosing and the need to reduce side effects. First-generation digital systems were developed to improve analgesics but are not always capable of making clinically relevant associations and do not necessarily lead to better clinical efficacy. A lack of improved clinical outcomes makes these systems less applicable for adoption by clinicians and patients. There is a need to enhance the therapeutic regimens of opioids. In the present paper, we present the use of a digital analgesic that consists of an analgesic administered under the control of a second-generation artificial intelligence system. Second-generation systems focus on improved patient outcomes measured based on clinical response and reduced side effects in a single subject. The algorithm regulates the administration of analgesics in a personalized manner. The digital analgesic provides advantages for both users and providers. The system enables dose optimization, improving effectiveness, and minimizing side effects while increasing adherence to beneficial therapeutic regimens. The algorithm improves the clinicians’ experience and assists them in managing chronic pain. The system reduces the financial burden on healthcare providers by lowering opioid-related morbidity and provides a market disruptor for pharma companies.

A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review

The plateau effect in training is a significant obstacle for professional athletes and average subjects. It evolves from both the muscle-nerve-axis-associated performance and various cardiorespiratory parameters. Compensatory adaptation mechanisms contribute to a lack of continuous improvement with most exercise regimens. Attempts to overcome this plateau in exercise have been only partially successful, and it remains a significant unmet need in both healthy subjects and those suffering from chronic neuromuscular, cardiopulmonary, and metabolic diseases. Variability patterns characterize many biological processes, from cellular to organ levels. The present review discusses the significant obstacles in overcoming the plateau in training and establishes a platform to implement subject-tailored variability patterns to prevent and overcome this plateau in muscle and cardiorespiratory performance.

Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response

Legalized use of cannabis products and the rising interest in their therapeutic benefits have opened up new opportunities for therapy and marketing. However, the marked variability in formulations, administration modes, therapeutic regimens, and inter- and intra-subject responses make the standardization of medical cannabis-based regimens difficult. Legalization has made the cannabis market highly competitive and lowered the revenue margins. This study reviews some of the challenges in medical cannabis use and difficulties in standardizing its therapeutic regimens that hinder maximizing its beneficial effects. The development of tolerance toward cannabis and low adherence to chronic administration further impair its long-term beneficial effects. Digital medical cannabis is a cannabis product controlled by a second-generation artificial intelligence (AI) system that improves patient responses by increasing adherence and dealing with tolerance. Second-generation AI systems focus on a single patient's outcome and deal with the inter- and intra-subject variability in responses. The use of digital medical cannabis is expected to improve product standardization, maximize therapeutic benefits, reduce health care costs, and increase the revenue of companies. Digital medical cannabis offers several market differentiators for cannabis companies. This study presents a model for promoting the use of digital medical cannabis and presents its advantages for patients, clinicians, health care authorities, insurance companies, and cannabis manufacturers. Ongoing trials and real-world data on the use of these systems further support the use of digital medical cannabis for improved global health.

A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage

Coronavirus disease 2019 (COVID-19), which is a respiratory illness associated with high mortality, has been classified as a pandemic. The major obstacles for the clinicians to contain the disease are limited information availability, difficulty in disease diagnosis, predicting disease prognosis, and lack of disease monitoring tools. Additionally, the lack of valid therapies has further contributed to the difficulties in containing the pandemic. Recent studies have reported that the dysregulation of the immune system leads to an ineffective antiviral response and promotes pathological immune response, which manifests as ARDS, myocarditis, and hepatitis. In this study, a novel platform has been described for disseminating information to physicians for the diagnosis and monitoring of patients with COVID-19. An adjuvant approach using compounds that can potentiate antiviral immune response and mitigate COVID-19-induced immune-mediated target organ damage has been presented. A prolonged beneficial effect is achieved by implementing algorithm-based individualized variability measures in the treatment regimen.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

Role of circadian rhythm and autonomic nervous system in liver function: a hypothetical basis for improving the management of hepatic encephalopathy

Hepatic encephalopathy (HE) is a common, incapacitating complication of cirrhosis that affects many patients with cirrhosis. Although 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 technique 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.

Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm

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.

Introducing variability in targeting the microtubules: Review of current mechanisms and future directions in colchicine therapy

Microtubules (MTs) are highly dynamic polymers that constitute the cellular cytoskeleton and play a role in multiple cellular functions. Variability characterizes biological systems and is considered a part of the normal function of cells and organs. Variability contributes to cell plasticity and is a mechanism for overcoming errors in cellular level assembly and function, and potentially the whole organ level. Dynamic instability is a feature of biological variability that characterizes the function of MTs. The dynamic behavior of MTs constitutes the basis for multiple biological processes that contribute to cellular plasticity and the timing of cell signaling. Colchicine is a MT‐modifying drug that exerts anti‐inflammatory and anti‐cancer effects. This review discusses some of the functions of colchicine and presents a platform for introducing variability while targeting MTs in intestinal cells, the microbiome, the gut, and the systemic immune system. This platform can be used for implementing novel therapies, improving response to chronic MT‐based therapies, overcoming drug resistance, exerting gut‐based systemic immune responses, and generating patient‐tailored dynamic therapeutic regimens.

Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity

Prevention of weight regain following successful weight loss is a major challenge in the treatment of obesity, irrespective of the weight reduction method used. The majority of individuals regain the lost weight over time; thus, achieving long-term sustainability in weight loss remains an unresolved issue. A compensatory adaptation to the weight loss methods occurs in several body organs and partly explains the lack of sustainable effect. Variability is inherent in many biological systems, and patterns of variability constitute a body mechanism that is active at several levels, starting from the genes and cellular pathways through to the whole-organ level. This study aimed to describe a platform that introduces individually tailored variability in vagal nerve stimulation and dietary regimen to ensure prolonged and sustainable weight loss and prevent weight regain. The platform is intended to provide a method that can overcome the body's compensatory adaptation mechanisms while ensuring a prolonged beneficial effect.

Adropin as a potential mediator of the metabolic system-autonomic nervous system-chronobiology axis: Implementing a personalized signature-based platform for chronotherapy

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 carbohydrate-lipid 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.

Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance

Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.

Role of the Immune System and the Circadian Rhythm in the Pathogenesis of Chronic Pancreatitis: Establishing a Personalized Signature for Improving the Effect of Immunotherapies for Chronic Pancreatitis

Pancreatitis, in both acute and chronic forms, poses a major therapeutic challenge and is associated with great morbidity and several complications. The nature of pancreatic injury in chronic pancreatitis (CP) and the wide range of causative processes that lead to CP have made effective therapy a true unmet need. Multiple physiological, genetic, environmental, and behavioral factors contribute to the development of CP. As a result, several fields of research are aimed at identifying and addressing the factors that contribute to pancreatic injury. In this article, we review the current understanding of the pathogenesis and natural history of CP. We focus on the autonomous nervous system, immune system, and role of a chronobiological therapeutic approach to alleviate symptoms and prevent or reverse pancreatic injury associated with CP. We aim to demonstrate that individualizing chronopharmacological treatments for CP is a promising direction for future treatment using immune, nervous, and circadian systems.

Overcoming Compensatory Mechanisms toward Chronic Drug Administration to Ensure Long-Term, Sustainable Beneficial Effects

Chronic administration of drugs leads to the activation of compensatory mechanisms that may inhibit some of their activity and induce unwanted toxicity. These mechanisms are an obstacle for maintaining a sustainable effect for many chronic medications. Pathways that adapt to the burden induced by chronic drugs, whether or not related to the underlying disease, can lead to a partial or complete loss of effect. Variability characterizes many biological systems and manifests itself as large intra- and inter-individual differences in the response to drugs. Circadian rhythm-based chronotherapy is further associated with variability in responses noted among patients. This paper reviews current knowledge regarding the loss of effect of chronic medications and the range of variabilities that have been described in responses and loss of responses. Establishment of a personalized platform for overcoming these prohibitive mechanisms is presented as a model for ensuring long-term sustained medication effects. This novel platform implements personalized variability signatures and individualized circadian rhythms for preventing and opposing the prohibitive effect of the compensatory mechanisms induced by chronic drug administration.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment

Sepsis remains a major therapeutic challenge and is associated with a high rate of morbidity and mortality. It is a dynamic condition in which multiple parameters change over time, rendering it difficult to overcome the various injurious responses, which worsen the prognosis in these patients. The prognosis of sepsis is associated with a disbalance of compensatory responses to infectious triggers, part of which can be deleterious. Marked inter- and intra-patient variability characterizes the mechanisms that underlie sepsis progression and determine the response to therapy. In this paper, we review some of the data on the use of chronopharmacological approaches for the treatment of patients with sepsis and discuss the role of the autonomic nervous system in the mechanisms associated with immune response and chronotherapy in these patients. We describe the implementation of an individualized platform that is based on the personalized autonomic nervous system, immune, and chronobiology-derived parameters for generating a patient-tailored therapeutic regimen. The notion of overcoming the deleterious compensatory response in a highly dynamic system in sepsis is presented to ensure an improved response to current therapies.

Introducing Patterns of Variability for Overcoming Compensatory Adaptation of the Immune System to Immunomodulatory Agents: A Novel Method for Improving Clinical Response to Anti-TNF Therapies

Primary lack of response and secondary loss of response (LOR) are major obstacles to the use of anti–tumor necrosis factor (TNF)-based therapies in patients with rheumatoid arthritis or inflammatory bowel disease. Here, we review the mechanisms and methods for predicting LOR and the currently used methods for overcoming the ineffectiveness of anti-TNFs. The complex functions of TNF and anti-TNF antibodies, which can promote both pro- or anti-inflammatory actions, and the factors that affect the induction of immune tolerance to their effects are presented. The lack of rules and the continuous dynamics of the immune processes partly underlie the unpredictability of the response to anti-TNFs. Variability is inherent to biological systems, including immune processes, and intra/inter-patient variability has been described in the response to drugs. This variability is viewed as a compensatory adaptation mechanism of the immune system in response to drugs and may contribute to treatment LOR. Dose reductions and drug holidays have been tested in patients treated with anti-TNFs. Regular dose-based regimens may be incompatible with physiological variability, further contributing to treatment inefficacy. We present the concept of overcoming immune system adaptation to anti-TNFs by introducing patient-tailored patterns of variability to treatment regimens.

β-Glycosphingolipids as Mediators of Both Inflammation and Immune Tolerance: A Manifestation of Randomness in Biological Systems

Plasticity in biological systems is attributed to the combination of multiple parameters which determine function. These include genotypic, phenotypic, and environmental factors. While biological processes can be viewed as ordered and sequential, biological randomness was suggested to underline part of them. The present review looks into the concept of randomness in biological systems by exploring the glycosphingolipids-NKT cells example. NKT cells are a unique subset of regulatory lymphocytes which play a role in both inflammation and tolerance. Glycosphingolipids promote an immune balance by changing different arms of the immune system in opposing environments. Traditional immunology looks at skewing the immune system into different directions by different types of activation of the same cell stimulation of different cells subsets, use of different ligands, or different the effect of different immune environments. While these may explain some of the effects, the lack of consistency and opposing results under similar settings may involve randomness which may also be part of real life effects of immunomodulatory agents. It means that several of the biological processes, cannot be explained by simple linear models, and may involve more complex concepts. The application for these concepts for improving therapies to patients with Gaucher disease are discussed. SUMMARY  The use of different ligands that target a variety of cell subsets in different immune environments may underlie differences in the functionality of NKT cells and their variability in response to NKT-based therapies. The novel concept of randomness in biology means that several biological processes cannot be solely explained by simple linear models and may instead involve much more complicated schemes of random disorder. These may have implications on future design of therapeutic regimens for improving the response to current treatments.