Oral Insulin Alleviates Liver Fibrosis and Reduces Liver Steatosis in Patients With Metabolic Dysfunction-associated Steatohepatitis and Type 2 Diabetes: Results of Phase II Randomized, Placebo-controlled Feasibility Clinical Trial.

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).

Improving the response to lenvatinib in partial responders using a Constrained-Disorder-Principle-based second-generation artificial intelligence-therapeutic regimen: a proof-of-concept open-labeled clinical trial.

Introduction: The main obstacle in treating cancer patients is drug resistance. Lenvatinib treatment poses challenges due to loss of response and the common dose-limiting adverse events (AEs). The Constrained-disorder-principle (CDP)-based second-generation artificial intelligence (AI) systems introduce variability into treatment regimens and offer a potential strategy for enhancing treatment efficacy. This proof-of-concept clinical trial aimed to assess the impact of a personalized algorithm-controlled therapeutic regimen on lenvatinib effectiveness and tolerability. Methods: A 14-week open-label, non-randomized trial was conducted with five cancer patients receiving lenvatinib-an AI-assisted application tailored to a personalized therapeutic regimen for each patient, which the treating physician approved. The study assessed changes in tumor response through FDG-PET-CT and tumor markers and quality of life via the EORTC QLQ-THY34 questionnaire, AEs, and laboratory evaluations. The app monitored treatment adherence. Results: At 14 weeks of follow-up, the disease control rate (including the following outcomes: complete response, partial response, stable disease) was 80%. The FDG-PET-CT scan-based RECIST v1.1 and PERCIST criteria showed partial response in 40% of patients and stable disease in an additional 40% of patients. One patient experienced a progressing disease. Of the participants with thyroid cancer, 75% showed a reduction in thyroglobulin levels, and 60% of all the participants showed a decrease in neutrophil-to-lymphocyte ratio during treatment. Improvement in the median social support score among patients utilizing the system supports an ancillary benefit of the intervention. No grade 4 AEs or functional deteriorations were recorded. Summary: The results of this proof-of-concept open-labeled clinical trial suggest that the CDP-based second-generation AI system-generated personalized therapeutic recommendations may improve the response to lenvatinib with manageable AEs. Prospective controlled studies are needed to determine the efficacy of this approach.

Investigating the Relationship between Chronic Liver Cirrhosis and Parkinsonism: A Comparative Analysis and a Suggested Diagnostic Scheme.

Aim: Neurological manifestations are common in patients with chronic liver diseases. This study aimed to depict the association between liver cirrhosis and Parkinson's disease (PD) and propose a clinically relevant diagnostic scheme. Methods: We examined patients' medical records with PD and chronic liver impairment secondary to cirrhosis or liver metastases for temporal correlations between liver insult and Parkinsonian signs. Results: Thirty-five individuals with PD and chronic liver impairment were included due to either cirrhosis or liver metastases. In all 22 patients with PD and liver metastases, the diagnosis of PD preceded the diagnosis of cancer. Conversely, patients with cirrhosis were often diagnosed with liver impairment before diagnosing PD. Age at diagnosis did not account for this difference. Conclusions: This study reinforces the potential clinical association between cirrhosis and PD. We also provide a diagnostic scheme that may guide therapeutic interventions and prognostic assessments.

Free Will as Defined by the Constrained Disorder Principle: a Restricted, Mandatory, Personalized, Regulated Process for Decision-Making.

The concept of free will has challenged physicists, biologists, philosophers, and other professionals for decades. The constrained disorder principle (CDP) is a fundamental law that defines systems according to their inherent variability. It provides mechanisms for adapting to dynamic environments. This work examines the CDP's perspective of free will concerning various free will theories. Per the CDP, systems lack intentions, and the "freedom" to select and act is built into their design. The "freedom" is embedded within the response range determined by the boundaries of the systems' variability. This built-in and self-generating mechanism enables systems to cope with perturbations. According to the CDP, neither dualism nor an unknown metaphysical apparatus dictates choices. Brain variability facilitates cognitive adaptation to complex, unpredictable situations across various environments. Human behaviors and decisions reflect an underlying physical variability in the brain and other organs for dealing with unpredictable noises. Choices are not predetermined but reflect the ongoing adaptation processes to dynamic prssu½res. Malfunctions and disease states are characterized by inappropriate variability, reflecting an inability to respond adequately to perturbations. Incorporating CDP-based interventions can overcome malfunctions and disease states and improve decision processes. CDP-based second-generation artificial intelligence platforms improve interventions and are being evaluated to augment personal development, wellness, and health.

A Feasibility Open-Labeled Clinical Trial Using a Second-Generation Artificial-Intelligence-Based Therapeutic Regimen in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy.

Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme ß-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings.

The Constrained Disorder Principle May Account for Consciousness.

There is still controversy surrounding the definition and mechanisms of consciousness. The constrained disorder principle (CDP) defines complex systems by their dynamic borders, limiting their inherent disorder. In line with the CDP, the brain exhibits a disorder bounded by dynamic borders essential for proper function, efficient energy use, and life support under continuous perturbations. The brain's inherent variability contributes to its adaptability and flexibility. Neuronal signal variability challenges the association of brain structures with consciousness and methods for assessing consciousness. The present paper discusses some theories about consciousness, emphasizing their failure to explain the brain's variability. This paper describes how the CDP accounts for consciousness's variability, complexity, entropy, and uncertainty. Using newly developed second-generation artificial intelligence systems, we describe how CDP-based platforms may improve disorders of consciousness (DoC) by accounting for consciousness variability, complexity, entropy, and uncertainty. This platform could be used to improve response to current interventions and develop new therapeutic regimens for patients with DoC in future studies.

Special Issue "Computer-Aided Drug Discovery and Treatment".

This Special Issue aims to highlight some of the latest developments in drug discovery [...].

Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized Second-generation Digital System.

INTRODUCTION: Low adherence to chronic treatment regimens is a significant barrier to improving clinical outcomes in patients with chronic diseases. Low adherence is a result of multiple factors. METHODS: We review the relevant studies on the prevalence of low adherence and present some potential solutions. RESULTS: This review presents studies on the current measures taken to overcome low adherence, indicating a need for better methods to deal with this problem. The use of first-generation digital systems to improve adherence is mainly based on reminding patients to take their medications, which is one of the reasons they fail to provide a solution for many patients. The establishment of a second-generation artificial intelligence system, which aims to improve the effectiveness of chronic drugs, is described. CONCLUSION: Improving clinically meaningful outcome measures and disease parameters may increase adherence and improve patients' response to therapy.

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.

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.

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.

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.

Increased hepatic Akt phosphorylation alleviated glucose intolerance and improved liver function in leptin-deficient mice.

Aim of the study: Akt is involved in upregulating the insulin-signaling pathways essential for maintaining glucose metabolism. Glycosphingolipids are involved in the pathogenesis of glucose intolerance and associated target organ injury. On the other hand, oral administration of b-glucosylceramide (GC) has been shown to alleviate insulin resistance. The present study aimed to determine the effects of oral administration of insulin and GC, separately and in combination, on Akt expression and the subsequent effect on metabolic syndrome characteristics in leptin-deficient mice. Material and methods: Four groups of leptin-deficient ob/ob mice were orally administered for four weeks: vehicle, GC, short-acting insulin, and GC combined with insulin. Mice were followed for hepatic Akt expression and changes in tumor necrosis factor a (TNF-a) level, hyperlipidemia, and liver damage. Results: In mice that received insulin or GC, particularly those that received both, the liver phosphorylation of Akt was significantly increased compared to those that received only vehicle. Serum TNF-a levels decreased in insulin-treated mice. These effects were associated with alleviating glucose intolerance and hyperlipidemia, as manifested by a significant glucose tolerance test improvement and reductions in serum triglyceride and cholesterol levels. Significant liver damage alleviation was noted by liver enzyme reductions in all treated groups, along with liver steatosis in the insulin-treated mice. Conclusions: These data established the potential use of oral insulin administration with glycosphingolipids to alleviate glucose intolerance and associated liver damage and hyperlipidemia via increased Akt expression in the liver. The data support targeting Akt as a potent therapeutic target for metabolic syndrome.

Serum Levels of Adropin Improve the Predictability of MELD and Child-Pugh Score in Cirrhosis: Results of Proof-of-Concept Clinical Trial.

Adropin is a peptide that was suggested to have a role in cirrhosis. The present study aimed to determine the ability to use serum adropin levels to improve their prediction accuracy as an adjunct to the current scores. In a single-center, proof-of-concept study, serum adropin levels were determined in thirty-three cirrhotic patients. The data were analyzed in correlation with Child-Pugh and MELD-Na scores, laboratory parameters, and mortality. Adropin levels were higher among cirrhotic patients that died within 180 days (1,325.7 ng/dL vs. 870.3 ng/dL, p = 0.024) and inversely correlated to the time until death (r 2 = 0.74). The correlation of adropin serum levels with mortality was better than MELD or Child-Pough scores (r 2 = 0.32 and 0.38, respectively). Higher adropin levels correlated with creatinine (r 2 = 0.79. p < 0.01). Patients with diabetes mellitus and cardiovascular diseases had elevated adropin levels. Integrating adropin levels with the Child-Pugh and MELD scores improved their correlation with the time of death (correlation coefficient: 0.91 vs. 0.38 and 0.67 vs. 0.32). The data of this feasibility study suggest that combining serum adropin with the Child-Pugh score and MELD-Na score improves the prediction of mortality in cirrhosis and can serve as a measure for assessing kidney dysfunction in these patients.

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.

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.

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-ß 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.

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.