Physiological Control Systems Research Articles

A multi-objective physiological control system for continuous-flow left ventricular assist device with non-invasive physiological feedback

In order to enable continuous-flow left ventricular assist device (CFLVAD) to adjust pump speed adaptively in response to changes in ventricular load, we developed a multi-objective physiological control system for CFLVAD with non-invasive physiological feedback mechanism, and proposed a multi-objective hierarchical control (MOHC) strategy to coordinate three controllers. A model-based estimation method was utilized to gain the physiological feedback variables. Suction prevention controller, regurgitation prevention controller, and physiological pulsatility controller were developed and applied to achieve the objectives: preventing left ventricular suction, preventing pump regurgitation, pump flow adaptive adjustment, and enhancing vascular pulsatility. The constraint conditions of feedback variables and controller priority were used to implement multi-objective hierarchical control. The preload and afterload were increased or decreased to simulate the scenarios of the sharp increase in left ventricular flow, the sharp decrease in left ventricular flow, and the rest-exercise-rest. The performance of MOHC was evaluated by subjecting it to three scenarios and compared with that of constant speed control (CSC) and pulsatile varying speed control (PVSC). The simulation results show that the linear correlation between the filtered and actual feedback variables was as high as 99.9%. Compared with CSC and PVSC, MOHC can avoid ventricular suction and pump regurgitation when the left ventricular flow changed rapidly. During rest or exercise, MOHC provided more adequate physiological perfusion and enhances greater vascular pulsatility than CSC and PVSC. MOHC can adjust the CFLVAD pump speed in response to changes in ventricular load.

Chaos in Physiological Control Systems: Health or Disease?

During the nineties, the Rössler’s have reported in their famous book “Chaos in Physiology,” that “physiology is the mother of Chaos.” Moreover, several researchers have proved that Chaos is a generic characteristic of systems in physiology. In the context of disease, like for example growth of cancer cell populations, Chaos often refers to irregular and unpredictable patterns. In such cases, Chaos signatures can be used to prove the existence of some pathologies. However, for other physiological behaviors, Chaos is a form of order disguised as disorder and can be a signature of healthy physiological functions. This is for example the case of human brain behavior. As the boundary between health and disease is not always clear-cut in chaotic systems in physiology, some conditions may involve transitions between ordered and chaotic states. Understanding these transitions and identifying critical points can be crucial for predicting Healthy vs. pathological Chaos. Using recent advances in physiological Chaos and disease dynamics, this survey paper tries to answer the crucial question: when Chaos be a sign of health or disease?

A General Approach for the Modelling of Negative Feedback Physiological Control Systems.

Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work's objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.

Frailty detection in older adults via fractal analysis of acceleration signals from wrist-worn sensors

PurposeFrailty is a reversible multidimensional syndrome that puts older people at a high risk of adverse health outcomes. It has been proposed to emerge from the dysregulation of the complex system dynamics of physiologic control systems. We propose the analysis of the fractal complexity of hand movements as a new method to detect frailty in older adults.MethodsFRAIL scale and Fried’s phenotype scores were calculated for 1209 subjects—72.4 (5.2) y.o. 569 women—and 1279 subjects—72.6 (5.3) y.o. 604 women—in the pubicly available NHANES 2011–2014 data set, respectively. The fractal complexity of their hand movements was assessed with a detrended fluctuation analysis (DFA) of their accelerometry records and a logistic regression model for frailty detection was fit.ResultsGoodness-of-fit to a power law was excellent (R2>0.98\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{2}>0.98$$\\end{document}). The association between complexity loss and frailty level was significant, Kruskal–Wallis test (df = 2, Chisq = 27.545, p-value <0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.001$$\\end{document}). The AUC of the logistic classifier was moderate (AUC with complexity = 0.69 vs. AUC without complexity = 0.67).ConclusionFrailty can be characterized in this data set with the Fried phenotype. Non-dominant hand movements in free-living conditions are fractal processes regardless of age or frailty level and its complexity can be quantified with the exponent of a power law. Higher levels of complexity loss are associated with higher levels of frailty. This association is not strong enough to justify the use of complexity loss after adjusting for sex, age, and multimorbidity.

Activation-inducible CAR expression enables precise control over engineered CAR T cell function

CAR T cell therapy is a rapidly growing area of oncological treatments having a potential of becoming standard care for multiple indications. Coincidently, CRISPR/Cas gene-editing technology is entering next-generation CAR T cell product manufacturing with the promise of more precise and more controllable cell modification methodology. The intersection of these medical and molecular advancements creates an opportunity for completely new ways of designing engineered cells to help overcome current limitations of cell therapy. In this manuscript we present proof-of-concept data for an engineered feedback loop. We manufactured activation-inducible CAR T cells with the help of CRISPR-mediated targeted integration. This new type of engineered T cells expresses the CAR gene dependent on their activation status. This artifice opens new possibilities to regulate CAR T cell function both in vitro and in vivo. We believe that such a physiological control system can be a powerful addition to the currently available toolbox of next-generation CAR constructs.

Adaptive closed-loop resuscitation controllers for hemorrhagic shock resuscitation.

After hemorrhage control, fluid resuscitation is the most important intervention for hemorrhage. Even skilled providers can find resuscitation challenging to manage, especially when multiple patients require care. In the future, attention-demanding medical tasks like fluid resuscitation for hemorrhage patients may be reassigned to autonomous medical systems when availability of skilled human providers is limited, such as in austere military settings and mass casualty incidents. Central to this endeavor is the development and optimization of control architectures for physiological closed-loop control systems (PCLCs). PCLCs can take many forms, from simple table look-up methods to widely used proportional-integral-derivative or fuzzy-logic control theory. Here, we describe the design and optimization of multiple adaptive resuscitation controllers (ARCs) that we have purpose-built for the resuscitation of hemorrhaging patients. Three ARC designs were evaluated that measured pressure-volume responsiveness using different methodologies during resuscitation from which adapted infusion rates were calculated. These controllers were adaptive in that they estimated required infusion flow rates based on measured volume responsiveness. A previously developed hardware-in-loop test platform was used to evaluate the ARCs implementations across several hemorrhage scenarios. After optimization, we found that our purpose-built controllers outperformed traditional control system architecture as embodied in our previously developed dual-input fuzzy-logic controller. Future efforts will focus on engineering our purpose-built control systems to be robust to noise in the physiological signal coming to the controller from the patient as well as testing controller performance across a range of test scenarios and in vivo.

A Physiological Control System for Pulsatile Ventricular Assist Device Using an Energy-Efficient Deep Reinforcement Learning Method

Pulsatile ventricular assist device (PVAD) is a blood pump used to assist the circulation support of the native heart. Because of patients’ complex physiological environment, PVAD’s control system requires high adaptive ability and low computational energy. However, traditional PID controllers do not possess sufficient adaptive ability. Although neural network controllers are with high adaptive ability, their extensive energy cost limits the applications. In this study, a PVAD physiological control system based on deep reinforcement leaming (DHL) is proposed, which significantly improves the system’s adaptive ability. To fiirther reduce its energy cost, a new energy-efficient DRL method, AddTD3, is developed, in which the fully connected network (FC) with high computation complexity is replaced by energy-efficient AdderNet. Experimental results show that the proposed AddTD3 controller is with higher adaptive ability than the traditional PID controller (cumulative absolute error: 237.1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mmHg</i> vs. 484.3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mmHg</i> ) and can be migrated to the mock circulation system without fine-tuning. It can reduce the energy cost of traditional DRL algorithm TD3 to 44.8% without reducing the performance (8773.2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pj</i> vs. 22420.4 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pj</i> ).

A Non-Invasive Physiological Control System of a Rotary Blood Pump Based on Preload Sensitivity: Use of Frank-Starling-Like Mechanism.

Implanting rotary blood pumps (RBPs) has become the principal treatment for patients suffering from severe heart failure. There are still many challenges to address for RBP control systems. These problems include meeting the patient's physiological perfusion, eliminating postoperative complications, as well as debugging the patient's physiological control system (automatically and indiscriminately). This paper proposes a non-invasive adaptive control system based on the Frank-Starling-like mechanism (NAC-FSL) to solve these problems. This control system uses the motor speed of the rotary blood pump as the only input variable, and the pump flow was estimated by the motor speed for achieving non-invasive detection. Simultaneously, a cardiovascular reference model was developed to provide an appropriate real-time preload for heart failure patients. The Frank-Starling-like control baseline was tracked to obtain the desired reference average pump flow by using the preload. Avoiding suction was done by adopting the control baseline (CLn), which included a flat slope under a high preload. Moreover, the NAC-FSL system could potentially unload the left ventricle and provide a higher pump flow with a smaller error during the exercise state, as compared to the CSC system. Finally, the K value indicating the preload sensitivity in the NAC-FSL controller was optimized to meet the perfusion needs according to the hemodynamic parameters.

In Vivo Evaluation of a Physiologic Control System for Rotary Blood Pumps Based on the Left Ventricular Pressure-Volume Loop.

Current generation continuous flow assist devices to operate at a fixed speed, which limits preload response and exercise capacity in left ventricular assist device (LVAD) patients. A feedback control system was developed to automatically adjust pump speed based on direct measurements of ventricular loading using a custom cannula tip with an integrated pressure sensor and volume-sensing conductance electrodes. The input to the control system is the integral of the left ventricular (LV) pressure versus conductance loop (PGA) over each cardiac cycle. The feedback control system adjusts pump speed based on the difference between the measured PGA and the desired PGA. The control system and cannula tip were tested in acute ovine studies (n = 5) using the HeartMate II LVAD. The preload response of the control system was evaluated by partially occluding and releasing the inferior vena cava using a vessel loop snare. The cannula tip was integrated onto a custom centrifugal flow LVAD and tested in a 14-day bovine study. The control system adjusted pump support to maintain constant ventricular loading: pump speed increased (decreased) following an increase (decrease) in preload. This study demonstrated in vivo the Starling-like response of an automatic pump control system based on direct measurements of LV loading.

Global sensitivity analysis in physiologically-based pharmacokinetic/pharmacodynamic models of inhaled and opioids anesthetics and its application to generate virtual populations.

The integration between physiologically-based pharmacokinetics (PBPK) models and pharmacodynamics (PD) models makes it possible to describe the absorption, distribution, metabolism and excretion processes of drugs, together with the concentration-response relationship, being a fundamental framework with wide applications in pharmacology. Nevertheless, the enormous complexity of PBPK models and the large number of parameters that define them leads to the need to study and understand how the uncertainty of the parameters affects the variability of the models output. To study this issue, this paper proposes a global sensitivity analysis (GSA) to identify the parameters that have the greatest influence on the response of the model. It has been selected as study cases the PBPK models of an inhaled anesthetic and an analgesic, along with two PD interaction models that describe two relevant clinical effects, hypnosis and analgesia during general anesthesia. The subset of the most relevant parameters found adequately with the GSA method has been optimized for the generation of a virtual population that represents the theoretical output variability of various model responses. The generated virtual population has the potential to be used for the design, development and evaluation of physiological closed-loop control systems.

Diguanylate Cyclase (DGC) Implicated in the Synthesis of Multiple Bacteriocins via the Flagellar-Type III Secretion System Produced by Pectobacterium carotovorum subsp. carotovorum.

The plant pathogen Pectobacterium carotovorum subsp. carotovorum (previously Erwinia carotovora subsp. carotovora) causes soft rot and stem rot diseases in a variety of crops, including Chinese cabbage, potato, and tomato. The flagellar-type III secretion systems were used by Pcc’s virulence mechanism to export proteins or bacteriocins to the outside of the cell. DGC, a virulence factor that cyclizes c-di-GMP, a common secondary signal in physiological processes and toxin control systems of many bacteria, was discovered in Pcc’s genomic DNA. The dgc gene in Pcc was blocked using the method of homologous recombination in our study. In the in vivo setting, the results demonstrated that the dgc knockout strain does not release low molecular weight bacteriocins. The bacteriocin gene (carocin S2, carocin S3, carocin S4) and the flagellar-type III secretion system genes were also unable to be transcribed by the dgc knockout strain in the transcription experiment. We also observed that the amount of bacteriocin expressed changed when the amount of L-glutamine in the environment exceeded a particular level. These data suggested that L-glutamine influenced physiological processes in Pcc strains in some way. We hypothesized a relationship between dgc and the genes involved in Pcc LMWB external export via the flagellar-type secretion system based on these findings. In this study, the current findings led us to propose a mechanism in which DGC’s cyclic di-GMP might bind to receptor proteins and positively regulate bacteriocin transcription as well as the synthesis, mobility, and transport of toxins.

Modeling and Analysis of Closed‐Loop Control of the Cardiovascular System

The baroreceptor reflex is a multi‐input, multi‐output physiological control system that regulates short‐term blood pressure by modulating parasympathetic and sympathetic nerve activity between the brainstem and the heart. The opposing effects of parasympathetic and sympathetic nerve activity work in conjunction to maintain cardiovascular homeostasis, with imbalances in activity associated with cardiovascular disease. Recently, attention has focused on the role of the intrinsic cardiac nervous system (ICN) in local control of nervous regulation of the heart and its role in balancing parasympathetic and sympathetic nerve activity. However, it is unknown how the ICN network structure contributes to integrative control of the heart. We formulated multiple alternative network options based on the anatomical, molecular and physiological evidence. We extended a quantitative closed‐loop computational model of the baroreceptor reflex by incorporating a high‐fidelity representation of the ICN to evaluate the impact of altered ICN network structures on overall cardiovascular control. The present computational model consists of (1) a system of ordinary differential equations to represent blood flow in the cardiovascular system, and (2) transfer function representations of sensory neurons, central nervous system neuronal groups, and ICN neuronal groups, connected in a closed‐loop control circuit. We use this model to investigate, via simulation, the role of the intrinsic cardiac nervous system in integrating and modulating parasympathetic and sympathetic nerve activity in healthy and diseased states. Our results show that the local circuit neurons may modulate the ICN network response to distinct vagal inputs towards the integrative control of local cardiac function.

Anatomical and Molecular Phenotypes of Fast and Slow Vagal Targets in the Intrinsic Cardiac Nervous System

The vagus nerve coordinates the communication between the brain and most of the visceral organs. Vagus nerve activity is critical to promote cardiac health and cardioprotective response in heart failure. The vagal motor outflow involves two nuclei, the nucleus ambiguous (NA) and the dorsal motor nucleus of the vagus (DMV). NA acts via nicotinic cholinergic processes to elicit fast kinetics of the intrinsic cardiac nervous system (ICN) response at the heart. By contrast, the DMV influences the ICN neurons via muscarinic cholinergic processes yielding slower kinetics of response. Interestingly, NA and DMV project to distinct neuronal populations in the ICN, providing an anatomical basis for the fast versus slow vagal action on the heart. We aim to delineate the ICN neuronal network mediating the two lanes of vagal inputs for regulating distinct cardiac functions with lane‐specific transcriptomic identities of ICN neuron targets. We performed immunohistochemistry (IHC) on the rat heart tissue sections embedded in the optimal cutting temperature (OCT) followed by confocal microscopy imaging. We found that while the ICN neurons express muscarinic and nicotinic acetylcholine receptors (AChRs) broadly across the ICN, with several neurons predominantly expressing either muscarinic or nicotinic AChRs. We used Laser Capture Microscopy (LCM) to acquire neurons that predominantly express muscarinic or nicotinic AChRs. We are profiling the transcriptomic identities of these LCM‐captured neuron classes. Our results suggest that mAChR‐only and nAChR‐only neurons differentially express neuromodulatory processes, including receptors, neurotransmitter synthesis enzymes, and neuropeptides. Further, we incorporate a systems approach combining spatial single‐cell transcriptomics, neuronal network modeling, and physiological control systems modeling to identify selective targets for novel neuromodulatory interventions that improve heart health.

Intelligent and strong robust CVS-LVAD control based on soft-actor-critic algorithm

Left ventricular assist device (LVAD) is an effective method to treat ventricular failure. According to the physiological conditions of different patients, the device adaptively adjusts its rotation speed to change LVAD output. In this study, a physiological control system for LVAD based on deep reinforcement learning (DRL) is proposed. The system estimates the amount of blood required by LVAD based on a Starling-like method. The DRL controller regulates LVAD to adjust the speed and quickly approach the target value. The changes of vascular resistance, myocardial contractility, and the transition from rest to exercise were simulated, and the single factor and mixed factor experiments were carried out to compare the effects of DRL controller and proportional integral derivative (PID) controller, which controls the system according to the difference between measured variables and expected values. Two metrics are used to illustrate the regulation effect: the sum of absolute error (SAE) and the response time of the two controllers, where SAE is the difference between the estimated required pumped blood flow LVADQe and the actual measured blood flow LVADQm. The experimental result shows that the SAE of the DRL controller is 47.6% of that of the PID controller, and the response time of the DRL controller is 38.6% of that of the PID controller. This study demonstrates that the LVAD based on the DRL controller can respond more quickly and more effectively to the different physiological needs of a variety of patients than a PID controller.

Modular Physiological Control for Left Ventricular Assist Devices: A Clinical Pilot Trial

<h3>Purpose</h3> Left Ventricular Assist Devices (LVAD) currently mainly operate at a constant set speed. Insufficient adaptation to physiologic demand is thought to be detrimental to patient outcome. A sensorless modular physiological control system for left ventricular assist devices was designed and its feasibility and safety evaluated in a clinical pilot trial. <h3>Methods</h3> Patients implanted with an LVAD (Medtronic HVAD) (n=6) were enrolled into a single-center, blinded, crossover, clinical pilot trial (NCT04786236), for a total of 13 sessions. They underwent a standardized protocol, consisting of orthostatic shifts (OR), Valsalva manoeuvres (VA) as well as short submaximal ergometry (ER). The protocol was performed in constant speed operation (CS) and in physiological control mode (PC) in randomized sequence. The modular controller consisted of heart rate, pulsatility and suction reaction modules, as well as a supervision module. A safety switch was installed for immediate transition to CS mode, in case of investigator safety concerns. <h3>Results</h3> The control system safely and autonomously adapted speed without the need for safety intervention. Speed correlated with posture resulting in lower speed compared to CS in the supine position by a median (interquartile range) of -128 (-158 to 31) rpm, while sitting by -75 (-137 to 65) rpm and during standing by -79 (-150 to -5) rpm. In 6 sessions suction occurred during the transition period of upwards orthostatic shifts in either mode, which was reduced in PC mode in 3 sessions and increased in 1 for a median difference of 0 (-5.75 to 1.75) suction events/minute. In VA, the time periods with the highest suction burden in both control modes was the late straining period / early recovery period. Speed was reduced in this phase compared to CS by -90 (-152 to -60) rpm, reducing the suction burden by a median of -1.2 (-5.9 to 0) suction events/minute. During ER, speed was increased by 86 (31 to 193) compared to CS, which resulted in a median flowrate increase of 0.2 (0.0 to 0.7) L/min. In 3 sessions speed could not be increased beyond CS set speed due to recurring suction episodes. In CS, the range of pump flowrate from the minimum during standing and the maximum during ER was 0.7 (0.5 to 1.2) L/min whereas in PC the range was increased to 1.4 (0.9 to 1.9) L/min. <h3>Conclusion</h3> Physiologic control without additional sensors is feasible and can adequately adapt speed to patient demand.

An Optimal H-Infinity Controller for Left Ventricular Assist Devices Based on a Starling-like Controller: A Simulation Study

Left ventricular assist devices (LVADs) are emerging innovations that provide a feasible alternative treatment for heart failure (HF) patients to enhance their quality of life. In this work, a novel physiological control system to optimize LVAD pump speed using an H-infinity controller was developed. The controller regulates the calculated target pump flow vs. measured pump flow to meet the changes in metabolic demand. The method proposes the implementation of the Frank–Starling mechanism (FSM) approach to control the speed of an LVAD using the left ventricle end-diastolic volume (Vlved) parameter (preload). An operating point was proposed to move between different control lines within the safe area to achieve the FSM. A proportional–integral (PI) controller was used to control the gradient angle between control lines to obtain the flow target. A lumped parameter model of the cardiovascular system was used to evaluate the proposed method. Exercise and rest scenarios were assessed under multi-physiological conditions of HF patients. Simulation results demonstrated that the control system was stable and feasible under different physiological states of the cardiovascular system (CVS). In addition, the proposed controller was able to keep hemodynamic variables within an acceptable range of the mean pump flow (Qp) (max = 5.2 L/min and min = 3.2 L/min) during test conditions.

Human Control Model Estimation in Physical Human-Machine Interaction: A Survey.

The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

Model-Free Adaptive Control of the Failing Heart Managed by Mechanical Supporting Devices

Left ventricular assist devices (LVADs)—mechanical circulatory pumps—have been used as a therapeutic option for patients who have progressed to the advanced stage of left-sided heart failure (HF), which is a chronic condition that the heart is unable to pump sufficient blood out of the left ventricle as it should. This surgically implanted device supports the native heart to pump blood from the heart to the remainder of the body to keep the patient alive as a temporary therapy until a donor's heart is available or as a destination therapy. However, the pump speed of the LVAD is typically set in a constant mode and cannot be freely changed. To promote the clinical use of LVADs as a long-term treatment option, a physiological control system is required to adaptively adjust the pump speed in response to time-varying blood demand. To this end, a model-free adaptive control (MFAC) algorithm is developed in this work, which estimates the control parameter over time using the data of manipulated and controlled variables (i.e., end-diastolic pressure of left ventricle and pump speed, respectively). In addition, since hemodynamic parameters in the cardiovascular system change over time due to short-term autonomic nerve regulation, we further considered baroreflex regulation to improve control performance; baroreflex regulation is one of the key factors of human's hemostatic mechanisms. The performance of the physiological controller is investigated and validated with computer simulations, which shows that the LVAD can respond to constant and time-varying blood demand.

Modeling and Analysis of the Intrinsic Cardiac Nervous System in Closed-Loop Cardiovascular Control

The baroreceptor reflex is a multi-input, multi-output physiological control system that regulates short-term blood pressure by modulating nerve activity between the brainstem and the heart. The computational model by Park et al. (2020) is the most recent iteration in our exploration of the system. However, the contributions of ”the little brain of the heart”, the intrinsic cardiac nervous system (ICN), to local control of the heart and to the integration of sensory information is unknown and has been overlooked in previous models. We have incorporated a high-fidelity representation of the ICN into a model of the baroreceptor reflex based on anatomical, molecular, and physiological evidence. The model consists of (1) differential equations to represent the cardiovascular system, and (2) transfer functions to represent neural control components, connected in a closed-loop control circuit. We use the model to evaluate the impact of alternative ICN network structures on overall cardiovascular control in response to mean arterial pressure and lung tidal volume perturbations. Our results show that the local circuit neurons that integrate sensory information into the ICN strengthen the response of ICN neuron activity, especially at low blood pressures, suggesting that the ICN amplifies the brainstem's response to perturbations.

Respiratory Complex I dysfunction in cancer: from a maze of cellular adaptive responses to potential therapeutic strategies.

Mitochondria act as key organelles in cellular bioenergetics and biosynthetic processes producing signals that regulate different molecular networks for proliferation and cell death. This ability is also preserved in pathologic contexts such as tumorigenesis, during which bioenergetic changes and metabolic reprogramming confer flexibility favoring cancer cell survival in a hostile microenvironment. Although different studies epitomize mitochondrial dysfunction as a protumorigenic hit, genetic ablation or pharmacological inhibition of respiratory complex I causing a severe impairment is associated with a low-proliferative phenotype. In this scenario, it must be considered that despite the initial delay in growth, cancer cells may become able to resume proliferation exploiting molecular mechanisms to overcome growth arrest. Here, we highlight the current knowledge on molecular responses activated by complex I-defective cancer cells to bypass physiological control systems and to re-adapt their fitness during microenvironment changes. Such adaptive mechanisms could reveal possible novel molecular players in synthetic lethality with complex I impairment, thus providing new synergistic strategies for mitochondrial-based anticancer therapy.