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

The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle

The Constrained Disorder Principle (CDP) offers a new framework for understanding how biological systems use and manage noise to maintain optimal functionality. This review explores the relationship between noise and biological systems at various scales, including genetic, cellular, and organ levels, and its implications for system malfunctions. According to the CDP, all systems require an optimal range of noise to function appropriately, and disease states can arise when these noise levels are disrupted. This review presents evidence supporting this principle across different biological contexts, such as genetic variability, cellular behavior, brain functions, human behavior, aging, evolution, and drug administration. For accurate clinical assessments, it is essential to distinguish between technical variability and intrinsic biological variability. When noise is adequately constrained, it serves as a fundamental mechanism for system adaptation and optimal functioning rather than simply a source of disruption. These findings have important implications for developing more effective therapeutic strategies and understanding biological systems' dynamics. CDP-based second-generation artificial intelligence systems can help regulate noise levels to address malfunctions. These systems have improved clinical outcomes in various conditions by incorporating controlled randomness. Understanding these patterns of variability has significant implications for diagnosis, treatment monitoring, and the development of more effective therapeutic strategies across various medical conditions.

The Constrained Disorder Principle: Beyond Biological Allostasis

The constrained disorder principle (CDP) defines complex biological systems based on inherent variability. Allostasis refers to the physiological processes that help maintain stability in response to changing environmental demands. Allostatic load describes the cumulative wear and tear on the body resulting from prolonged exposure to stress, and it has been suggested to mediate the relationship between stress and disease. This study presents the concepts of CDP and allostasis while discussing their similarities and differences. We reviewed the current literature on the potential benefits of introducing controlled doses of biological noise into interventions, which may enhance the effectiveness of therapies. The paper highlights the promising role of variability provided by a CDP-based second-generation artificial intelligence system in improving health outcomes.

Inter-organ correlations in inflammation regulation: a novel biological paradigm in a murine model

Interactions between immune system constituents are mediated through direct contact or the transfer of mediators. The study aimed to assess the correlation between system components and out-of-body signals in a model of liver inflammation. In the first experiment, mice injected with Concanavalin A (ConA) were housed in a cage with a tube on top containing healthy livers or livers harvested from mice injected with ConA. In the second experiment, mice were housed in a cage with a tube that contained splenocytes harvested from naïve donors or from naïve donors treated in vitro with dexamethasone. Mice were tested for serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. External whole livers and spleens influenced the immune-mediated inflammatory response of mice. When ConA-injected mice were housed in cages with tubes containing livers harvested from naïve mice, ALT serum levels were significantly reduced. ALT serum levels were significantly elevated when mice were kept in cages with a tube containing livers harvested from ConA-injected mice. In the second part of the experiment, mice injected with ConA and housed in cages with a tube on top that contained splenocytes harvested from naïve donors had increased ALT levels. Similarly, mice with tubes containing splenocytes from dexamethasone-treated naïve donors also showed elevated ALT levels. The data suggest that correlations between immune system constituents can be established using out-of-body whole livers or spleens without contact or transfer of mediators.

The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems

Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP, intrinsic unpredictability is mandatory for the dynamicity of biological systems under continuously changing internal and external perturbations. The present paper describes some of the parameters and challenges associated with uncertainty and randomness in biological systems and presents methods for quantifying them. Modeling biological systems necessitates accounting for the randomness, variability, and underlying uncertainty of systems in health and disease. The CDP provides a scheme for dealing with uncertainty in biological systems and sets the basis for using them. This paper presents the CDP-based second-generation artificial intelligence system that incorporates variability to improve the effectiveness of medical interventions. It describes the use of the digital pill that comprises algorithm-based personalized treatment regimens regulated by closed-loop systems based on personalized signatures of variability. The CDP provides a method for using uncertainties in complex systems in an outcome-based manner.

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.

The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms

Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant.

Role of Cytoskeletal Elements in Regulation of Synaptic Functions: Implications Toward Alzheimer's Disease and Phytochemicals-Based Interventions

Alzheimer's disease (AD), a multifactorial disease, is characterized by the accumulation of neurofibrillary tangles (NFTs) and amyloid beta (Aβ) plaques. AD is triggered via several factors like alteration in cytoskeletal proteins, a mutation in presenilin 1 (PSEN1), presenilin 2 (PSEN2), amyloid precursor protein (APP), and post-translational modifications (PTMs) in the cytoskeletal elements. Owing to the major structural and functional role of cytoskeletal elements, like the organization of axon initial segmentation, dendritic spines, synaptic regulation, and delivery of cargo at the synapse; modulation of these elements plays an important role in AD pathogenesis; like Tau is a microtubule-associated protein that stabilizes the microtubules, and it also causes inhibition of nucleo-cytoplasmic transportation by disrupting the integrity of nuclear pore complex. One of the major cytoskeletal elements, actin and its dynamics, regulate the dendritic spine structure and functions; impairments have been documented towards learning and memory defects. The second major constituent of these cytoskeletal elements, microtubules, are necessary for the delivery of the cargo, like ion channels and receptors at the synaptic membranes, whereas actin-binding protein, i.e., Cofilin's activation form rod-like structures, is involved in the formation of paired helical filaments (PHFs) observed in AD. Also, the glial cells rely on their cytoskeleton to maintain synaptic functionality. Thus, making cytoskeletal elements and their regulation in synaptic structure and function as an important aspect to be focused for better management and targeting AD pathology. This review advocates exploring phytochemicals and Ayurvedic plant extracts against AD by elucidating their neuroprotective mechanisms involving cytoskeletal modulation and enhancing synaptic plasticity. However, challenges include their limited bioavailability due to the poor solubility and the limited potential to cross the blood-brain barrier (BBB), emphasizing the need for targeted strategies to improve therapeutic efficacy.

Synthesis and Biological Evaluation of Novel 2-Aroyl Benzofuran-Based Hydroxamic Acids as Antimicrotubule Agents

Because of synergism between tubulin and HDAC inhibitors, we used the pharmacophore fusion strategy to generate potential tubulin-HDAC dual inhibitors. Drug design was based on the introduction of a N-hydroxyacrylamide or a N-hydroxypropiolamide at the 5-position of the 2-aroylbenzo[b]furan skeleton, to produce compounds 6a-i and 11a-h, respectively. Among the synthesized compounds, derivatives 6a, 6c, 6e, 6g, 11a, and 11c showed excellent antiproliferative activity, with IC50 values at single- or double-digit nanomolar levels, against the A549, HT-29, and MCF-7 cells resistant towards the control compound combretastatin A-4 (CA-4). Compounds 11a and 6g were also 10-fold more active than CA-4 against the Hela cell line. When comparing the inhibition of tubulin polymerization versus the HDAC6 inhibitory activity, we found that 6a-g, 6i, 11a, 11c, and 11e, although very potent as inhibitors of tubulin assembly, did not have significant inhibitory activity against HDAC6.

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 β-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.

Spatially modulated ablation driven by chaotic attractors in human lung epithelial cancer cells

A significant modification in photoinduced energy transfer in cancer cells is reported by the assistance of a dynamic modulation of the beam size of laser irradiation. Human lung epithelial cancer cells in monolayer form were studied. In contrast to the quantum and thermal ablation effect promoted by a standard focused Gaussian beam, a spatially modulated beam can caused around 15% of decrease in the ablation threshold and formation of a ring-shaped distribution of the photothermal transfer effect. Optical irradiation was conducted in A549 cells by a 532 nm single-beam emerging from a Nd:YVO4 system. Ablation effects derived from spatially modulated convergent waves were controlled by an electrically focus-tunable lens. The proposed chaotic behavior of the spatial modulation followed an Arneodo chaotic oscillator. Fractional dynamic thermal transport was analyzed in order to describe photoenergy in propagation through the samples. Immediate applications of chaos theory for developing phototechnology devices driving biological functions or phototherapy treatments can be considered.

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.

Physiological roles of chloride ions in bodily and cellular functions

Physiological roles of Cl-, a major anion in the body, are not well known compared with those of cations. This review article introduces: (1) roles of Cl- in bodily and cellular functions; (2) the range of cytosolic Cl- concentration ([Cl-]c); (3) whether [Cl-]c could change with cell volume change under an isosmotic condition; (4) whether [Cl-]c could change under conditions where multiple Cl- transporters and channels contribute to Cl- influx and efflux in an isosmotic state; (5) whether the change in [Cl-]c could be large enough to act as signals; (6) effects of Cl- on cytoskeletal tubulin polymerization through inhibition of GTPase activity and tubulin polymerization-dependent biological activity; (7) roles of cytosolic Cl- in cell proliferation; (8) Cl--regulatory mechanisms of ciliary motility; (9) roles of Cl- in sweet/umami taste receptors; (10) Cl--regulatory mechanisms of with-no-lysine kinase (WNK); (11) roles of Cl- in regulation of epithelial Na+ transport; (12) relationship between roles of Cl- and H+ in body functions.

Non-chemical signalling between mitochondria

A wide variety of studies have reported some form of non-chemical or non-aqueous communication between physically isolated organisms, eliciting changes in cellular proliferation, morphology, and/or metabolism. The sources and mechanisms of such signalling pathways are still unknown, but have been postulated to involve vibration, volatile transmission, or light through the phenomenon of ultraweak photon emission. Here, we report non-chemical communication between isolated mitochondria from MCF7 (cancer) and MCF10A (non-cancer) cell lines. We found that mitochondria in one cuvette stressed by an electron transport chain inhibitor, antimycin, alters the respiration of mitochondria in an adjacent, but chemically and physically separate cuvette, significantly decreasing the rate of oxygen consumption compared to a control (p = <0.0001 in MCF7 and MCF10A mitochondria). Moreover, the changes in O2-consumption were dependent on the origin of mitochondria (cancer vs. non-cancer) as well as the presence of "ambient" light. Our results support the existence of non-chemical signalling between isolated mitochondria. The experimental design suggests that the non-chemical communication is light-based, although further work is needed to fully elucidate its nature.

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.

X-ray Crystal Structure-Guided Discovery of Novel Indole Analogues as Colchicine-Binding Site Tubulin Inhibitors with Immune-Potentiating and Antitumor Effects against Melanoma

A series of novel indole analogues were discovered as colchicine-binding site inhibitors of tubulin. Among them, 3a exhibited the highest antiproliferative activity (average IC₅₀ = 4.5 nM), better than colchicine (IC₅₀ = 65.3 nM). The crystal structure of 3a in complex with tubulin was solved by X-ray crystallography, which explained the improved binding affinity of 3a to tubulin and thus its higher anticancer activity (IC₅₀ = 4.5 nM) than the lead compound 12b (IC₅₀ = 32.5 nM). In vivo, 3a (5 mg/kg) displayed significant antitumor efficacy against B16-F10 melanoma with a TGI of 62.96% and enhanced the antitumor efficacy of a small-molecule PD-1/PD-L1 inhibitor NP19 (TGI = 77.85%). Moreover, 3a potentiated the antitumor immunity of NP19 by activating the tumor immune microenvironment, as demonstrated by the increased tumor-infiltrating lymphocytes (TIL). Collectively, this work shows a successful example of crystal structure-guided discovery of a novel tubulin inhibitor 3a as a potential anticancer and immune-potentiating agent.

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.

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.

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.