Progressive Cardiac Metabolic Defects Accompany Diastolic and Severe Systolic Dysfunction in Spontaneously Hypertensive Rat Hearts

Background Cardiac metabolic abnormalities are present in heart failure. Few studies have followed metabolic changes accompanying diastolic and systolic heart failure in the same model. We examined metabolic changes during the development of diastolic and severe systolic dysfunction in spontaneously hypertensive rats (SHR). Methods and Results We serially measured myocardial glucose uptake rates with dynamic 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography in vivo in 9-, 12-, and 18-month-old SHR and Wistar Kyoto rats. Cardiac magnetic resonance imaging determined systolic function (ejection fraction) and diastolic function (isovolumetric relaxation time) and left ventricular mass in the same rats. Cardiac metabolomics was performed at 12 and 18 months in separate rats. At 12 months, SHR hearts, compared with Wistar Kyoto hearts, demonstrated increased isovolumetric relaxation time and slightly reduced ejection fraction indicating diastolic and mild systolic dysfunction, respectively, and higher (versus 9-month-old SHR decreasing) 2-[18F] fluoro-2-deoxy-d-glucose uptake rates (Ki). At 18 months, only few SHR hearts maintained similar abnormalities as 12-month-old SHR, while most exhibited severe systolic dysfunction, worsening diastolic function, and markedly reduced 2-[18F] fluoro-2-deoxy-d-glucose uptake rates. Left ventricular mass normalized to body weight was elevated in SHR, more pronounced with severe systolic dysfunction. Cardiac metabolite changes differed between SHR hearts at 12 and 18 months, indicating progressive defects in fatty acid, glucose, branched chain amino acid, and ketone body metabolism. Conclusions Diastolic and severe systolic dysfunction in SHR are associated with decreasing cardiac glucose uptake, and progressive abnormalities in metabolite profiles. Whether and which metabolic changes trigger progressive heart failure needs to be established.

Metformin Improves Cardiac Metabolism and Function, and Prevents Left Ventricular Hypertrophy in Spontaneously Hypertensive Rats

Background

In spontaneously hypertensive rats (SHR) we observed profound myocardial metabolic changes during early hypertension before development of cardiac dysfunction and left ventricular hypertrophy. In this study, we evaluated whether metformin improved myocardial metabolic abnormalities and simultaneously prevented contractile dysfunction and left ventricular hypertrophy in SHR.

Methods and Results

SHR and control Wistar–Kyoto rats were treated with metformin from 2 to 5 months of age, when SHR hearts exhibit metabolic abnormalities and develop cardiac dysfunction and left ventricular hypertrophy. We evaluated the effect of metformin on myocardial glucose uptake rates with dynamic 2‐[¹⁸F] fluoro‐2‐deoxy‐D‐glucose positron emission tomography. We used cardiac MRI in vivo to assess the effect of metformin on ejection fraction, left ventricular mass, and end‐diastolic wall thickness, and also analyzed metabolites, AMP‐activated protein kinase and mammalian target‐of‐rapamycin activities, and mean arterial blood pressure. Metformin‐treated SHR had lower mean arterial blood pressure but remained hypertensive. Cardiac glucose uptake rates, left ventricular mass/tibia length, wall thickness, and circulating free fatty acid levels decreased to normal, and ejection fraction improved in treated SHR. Hearts of treated SHR exhibited increased AMP‐activated protein kinase phosphorylation and reduced mammalian target‐of‐rapamycin activity. Cardiac metabolite profiling demonstrated that metformin decreased fatty acyl carnitines and markers of oxidative stress in SHR.

Conclusions

Metformin reduced blood pressure, normalized myocardial glucose uptake, prevented left ventricular hypertrophy, and improved cardiac function in SHR. Metformin may exert its effects by normalizing myocardial AMPK and mammalian target‐of‐rapamycin activities, improving fatty acid oxidation, and reducing oxidative stress. Thus, metformin may be a new treatment to prevent or ameliorate chronic hypertension–induced left ventricular hypertrophy.

Metabolic Changes in Spontaneously Hypertensive Rat Hearts Precede Cardiac Dysfunction and Left Ventricular Hypertrophy

Background

Sustained pressure overload leads to changes in cardiac metabolism, function, and structure. Both time course and causal relationships between these changes are not fully understood. Therefore, we studied spontaneously hypertensive rats (SHR) during early hypertension development and compared them to control Wistar Kyoto rats.

Methods and Results

We serially evaluated myocardial glucose uptake rates (Ki) with dynamic 2‐[¹⁸F] fluoro‐2‐deoxy‐D‐glucose positron emission tomography, and ejection fraction and left ventricular mass to body weight ratios with cardiac magnetic resonance imaging in vivo, determined glucose uptake and oxidation rates in isolated perfused hearts, and analyzed metabolites, mammalian target of rapamycin activity and endoplasmic reticulum stress in dissected hearts. When compared with Wistar Kyoto rats, SHR demonstrated increased glucose uptake rates (Ki) in vivo, and reduced ejection fraction as early as 2 months of age when hypertension was established. Isolated perfused SHR hearts showed increased glucose uptake and oxidation rates starting at 1 month. Cardiac metabolite analysis at 2 months of age revealed elevated pyruvate, fatty acyl‐ and branched chain amino acid‐derived carnitines, oxidative stress, and inflammation. Mammalian target of rapamycin activity increased in SHR beginning at 2 months. Left ventricular mass to body weight ratios and endoplasmic reticulum stress were elevated in 5 month‐old SHR.

Conclusions

Thus, in a genetic hypertension model, chronic cardiac pressure overload promptly leads to increased myocardial glucose uptake and oxidation, and to metabolite abnormalities. These coincide with, or precede, cardiac dysfunction while left ventricular hypertrophy develops only later. Myocardial metabolic changes may thus serve as early diagnostic markers for hypertension‐induced left ventricular hypertrophy.

Serial MRI Imaging Reveals Minimal Impact of Ketogenic Diet on Established Liver Tumor Growth

Rodent models of liver tumorigenesis have reproducibly shown that dietary sugar intake is a powerful driver of liver tumor initiation and growth. In contrast, dietary sugar restriction with ketogenic diets or calorie restriction generally prevents liver tumor formation. Ketogenic diet is viewed positively as a therapeutic adjuvant; however, most ketogenic diet studies described to date have been performed in prevention mode rather than treatment mode. Therefore, it remains unclear whether a ketogenic diet can be administered in late stages of disease to stall or reverse liver tumor growth. To model the clinically relevant treatment mode, we administered a ketogenic diet to mice after liver tumor initiation and monitored tumor growth by magnetic resonance imaging (MRI). Male C57BL/6 mice were injected with diethylnitrosamine (DEN) at 2 weeks of age and fed a chow diet until 39 weeks of age, when they underwent MRI imaging to detect liver tumors. Mice were then randomised into two groups and fed either a chow diet or switched to a ketogenic diet from 40⁻48 weeks of age. Serial MRIs were performed at 44 and 48 weeks of age. All mice had tumors at study completion and there were no differences in total tumor burden between diet groups. Although a ketogenic diet has marked protective effects against DEN-induced liver tumourigenesis in this mouse model, these data demonstrate that ketogenic diet cannot stop the progression of established liver tumors.

Glucose regulation of load-induced mTOR signaling and ER stress in mammalian heart

BACKGROUND

Changes in energy substrate metabolism are first responders to hemodynamic stress in the heart. We have previously shown that hexose-6-phosphate levels regulate mammalian target of rapamycin (mTOR) activation in response to insulin. We now tested the hypothesis that inotropic stimulation and increased afterload also regulate mTOR activation via glucose 6-phosphate (G6P) accumulation.

METHODS AND RESULTS

We subjected the working rat heart ex vivo to a high workload in the presence of different energy-providing substrates including glucose, glucose analogues, and noncarbohydrate substrates. We observed an association between G6P accumulation, mTOR activation, endoplasmic reticulum (ER) stress, and impaired contractile function, all of which were prevented by pretreating animals with rapamycin (mTOR inhibition) or metformin (AMPK activation). The histone deacetylase inhibitor 4-phenylbutyrate, which relieves ER stress, also improved contractile function. In contrast, adding the glucose analogue 2-deoxy-d-glucose, which is phosphorylated but not further metabolized, to the perfusate resulted in mTOR activation and contractile dysfunction. Next we tested our hypothesis in vivo by transverse aortic constriction in mice. Using a micro-PET system, we observed enhanced glucose tracer analog uptake and contractile dysfunction preceding dilatation of the left ventricle. In contrast, in hearts overexpressing SERCA2a, ER stress was reduced and contractile function was preserved with hypertrophy. Finally, we examined failing human hearts and found that mechanical unloading decreased G6P levels and ER stress markers.

CONCLUSIONS

We propose that glucose metabolic changes precede and regulate functional (and possibly also structural) remodeling of the heart. We implicate a critical role for G6P in load-induced mTOR activation and ER stress.

Three-dimensional phase contrast angiography of the mouse aortic arch using spiral MRI

Atherosclerosis is a complex disease whose spatial distribution is hypothesized to be influenced by the local hemodynamic environment. The use of transgenic mice provides a mechanism to study the relationship between hemodynamic forces, most notably wall shear stress (WSS), and the molecular factors that influence the disease process. Phase contrast MRI using rectilinear trajectories has been used to measure boundary conditions for use in computational fluid dynamic models. However, the unique flow environment of the mouse precludes use of standard imaging techniques in complex, curved flow regions such as the aortic arch. In this article, two-dimensional and three-dimensional spiral cine phase contrast sequences are presented that enable measurement of velocity profiles in curved regions of the mouse vasculature. WSS is calculated directly from the spatial velocity gradient, enabling WSS calculation with a minimal set of assumptions. In contrast to the outer radius of the aortic arch, the inner radius has a lower time-averaged longitudinal WSS (7.06 ± 0.76 dyne/cm(2) vs. 18.86 ± 1.27 dyne/cm(2) ; P < 0.01) and higher oscillatory shear index (0.14 ± 0.01 vs. 0.08 ± 0.01; P < 0.01). This finding is in agreement with humans, where WSS is lower and more oscillatory along the inner radius, an atheroprone region, than the outer radius, an atheroprotective region.

Postinfarction myocardial scarring in mice: molecular MR imaging with use of a collagen-targeting contrast agent

PURPOSE

To prospectively evaluate a gadolinium-based collagen-targeting contrast agent, EP-3533, for in vivo magnetic resonance (MR) imaging of myocardial fibrosis in a mouse model of healed myocardial infarction (MI).

MATERIALS AND METHODS

All procedures were performed in accordance with protocols approved by the animal care and use committee. MI was induced in eight mice by means of occlusion of the left anterior descending coronary artery followed by reperfusion. Four MR examinations were performed in each animal: one examination before, one examination 1 day after, and two examinations 6 weeks after the MI. For the latter two examinations, electrocardiographically gated inversion-recovery gradient-echo MR images were acquired before and serially (every 5 minutes) after the intravenous injection of either gadopentetate dimeglumine or EP-3533. The image enhancement kinetic properties of the postinfarction scar, normal myocardium, and blood were compared.

RESULTS

Dynamic T1-weighted MR imaging revealed the washout time constants for EP-3533 to be significantly longer than those for gadopentetate dimeglumine in regions of postinfarction scarring (mean, 194.8 minutes +/-116.8 [standard deviation] vs 25.5 minutes +/- 4.2; P < .05) and in normal myocardium (mean, 45.4 minutes +/- 16.7 vs 25.1 minutes +/- 9.7; P < .05). Findings on postmortem histologic sections stained for collagen correlated well with EP-3533-enhanced areas seen on inversion-recovery MR images. Fifty minutes after EP-3533 injection, the postinfarction scar tissue samples, as compared with the normal myocardium, had a twofold higher concentration of gadolinium.

CONCLUSION

Use of the gadolinium-based collagen-targeting contrast agent, EP-3533, enabled in vivo molecular MR imaging of fibrosis in a mouse model of healed postinfarction myocardial scarring.

EEG complexity as a measure of depth of anesthesia for patients

A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.

Discrimination of anesthetic states using mid-latency auditory evoked potential and artificial neural networks

This study was undertaken to determine whether artificial neural network (ANN) processing of mid-latency auditory evoked potentials (MLAEPs) can identify different anesthetic states during propofol anesthesia, and to determine those parameters that are most useful in the identification process. Twenty-one patients undergoing elective abdominal surgery were studied. To maintain general anesthesia, the patients received propofol (3-5 mgkg(-1) h(-1) intravenously). Epidural analgesia at the level of T4-5 blocked painful stimuli. MLAEP was recorded continuously with patients awake, during induction, during maintenance of general anesthesia, and during emergence until the patients were recovered from anesthesia. Latencies of the 5 MLAEP peaks and three peak to peak amplitudes were measured, along with hemodynamic parameters (heart rate, systolic, and diastolic arterial blood pressure). Four-layer ANNs were used to model the relationship between the parameters of the MLAEP and the four different states (awake, adequate anesthesia, during/before intraoperative movement, and emergence from anesthesia). The best identification accuracy was obtained using only the five latencies. The combination of five latencies and three amplitudes did not improve the identification accuracy. Use of the only the three hemodynamic parameters produced a much poorer identification. This study suggests that the MLAEP has useful information for identifying different anesthetic states, especially in its latencies. A nonlinear discrimination approach, such as the ANN, can effectively capture the relation between the MLAEP patterns and the different states of anesthesia.

Derived fuzzy knowledge model for estimating the depth of anesthesia

Reliable and noninvasive monitoring of the depth of anesthesia (DOA) is highly desirable. Based on adaptive network-based fuzzy inference system (ANFIS) modeling, a derived fuzzy knowledge model is proposed for quantitatively estimating the DOA and validate it by 30 experiments using 15 dogs undergoing anesthesia with three different anesthetic regimens (propofol, isoflurane, and halothane). By eliciting fuzzy if-then rules, the model provides a way to address the DOA estimation problem by using electroencephalogram-derived parameters. The parameters include two new measures (complexity and regularity) extracted by nonlinear quantitative analyses, as well as spectral entropy. The model demonstrates good performance in discriminating awake and asleep states for three common anesthetic regimens (accuracy 90.3 % for propofol, 92.7 % for isoflurane, and 89.1% for halothane), real-time feasibility, and generalization ability (accuracy 85.9% across the three regimens). The proposed fuzzy knowledge model is a promising candidate as an effective tool for continuous assessment of the DOA.

Simultaneous regulation of hemodynamic and anesthetic states: a simulation study

A model predictive control strategy to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is presented. A nonlinear canine circulatory model, which has been used to study the effect of inotropic and vasoactive drugs on hemodynamic variables, has been extended to include propofol pharmacokinetics and pharmacodynamics. Propofol blood concentration is used as a measure for depth of anesthesia. The simulation model is used to design and test the control strategy. The optimization-based model predictive control strategy assures that constraints imposed on the drug infusion rates are met. The physician always remains "in the loop" and serves as the "primary controller" by making propofol blood concentration setpoint changes based on observations about anesthetic depth. Results are shown for three simulated cases: (i) congestive heart failure, (ii) postcoronary artery bypass, and (iii) acute changes in hemodynamic variables.

Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic

A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

Control of a nonsquare drug infusion system: A simulation study

A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post-operative hypertension, and sepsis shock were studied in closed-loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such as drug dosage specifications, commonly desired by critical care physicians.

Predicting movement during anaesthesia by complexity analysis of electroencephalograms

A new approach to predicting movement during anaesthesia by using complexity analysis of electroencephalograms (EEG) signals is presented. The raw EEG signal is first decomposed into six consecutive different scaling components by wavelet transform on the basis of its self-similarity. The Lempel-Ziv complexity measures C(n) are extracted from the raw EEG and its corresponding components by complexity analysis. Prediction of movement during anaesthesia is then made by a four-layer artificial neural network (ANN) using the C(n)s. The combination of these three different approaches enables the system to address the non-analytical, non-stationary, non-linear and dynamical properties of the EEG. From 20 dog experiments, 109 distinct EEG recordings are collected under isoflurane anaesthesia. Testing the ANN using the 'drop one dog' method, the performance obtained for the system in detecting movement is: sensitivity 88%, specificity 97% and accuracy 92%. Comparisons with other methods, such as spectral edge frequency, median frequency and principal component analysis, show that the proposed system has a certain advantage. This new method is computationally fast and well suited for realtime clinical implementation.

The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia

The objective of this study was to design and evaluate a methodology for estimating the depth of anesthesia in a canine model that integrates electroencephalogram (EEG)-derived autoregressive (AR) parameters, hemodynamic parameters, and the alveolar anesthetic concentration. Using a parameters, and the alveolar anesthetic concentration. Using a parametric approach, two separate AR models of order ten were derived for the EEG, one from the third-order cumulant sequence and the other from the autocorrelation lags of the EEG. Since the anesthetic dose versus depth of anesthesia curve is highly nonlinear, a neural network (NN) was chosen as the basic estimator and a multiple NN approach was conceived which took hemodynamic parameters, EEG derived parameters, and anesthetic concentration as input feature vectors. Since the estimation of the depth of anesthesia involves cognitive as well as statistical uncertainties, a fuzzy integral was used to integrate the individual estimates of the various networks and to arrive at the final estimate of the depth of anesthesia. Data from 11 experiments were used to train the NN's which were then tested on nine other experiments. The fuzzy integral of the individual NN estimates (when tested on 43 feature vectors from seven of the nine test experiments) classified 40 (93%) of them correctly, offering a substantial improvement over the individual NN estimates.

Depth of anesthesia estimation and control

A fully automated system was developed for the depth of anesthesia estimation and control with the intravenous anesthetic, Propofol. The system determines the anesthesia depth by assessing the characteristics of the mid-latency auditory evoked potentials (MLAEP). The discrete time wavelet transformation was used for compacting the MLAEP which localizes the time and the frequency of the waveform. Feature reduction utilizing step discriminant analysis selected those wavelet coefficients which best distinguish the waveforms of those responders from the nonresponders. A total of four features chosen by such analysis coupled with the Propofol effect-site concentration were used to train a four-layer artificial neural network for classifying between the responders and the nonresponders. The Propofol is delivered by a mechanical syringe infusion pump controlled by Stanpump which also estimates the Propofol effect-site and plasma concentrations using a three-compartment pharmacokinetic model with the Tackley parameter set. In the animal experiments on dogs, the system achieved a 89.2% accuracy rate for classifying anesthesia depth. This result was further improved when running in real-time with a confidence level estimator which evaluates the reliability of each neural network output. The anesthesia level is adjusted by scheduled incrementation and a fuzzy-logic based controller which assesses the mean arterial pressure and/or the heart rate for decrementation as necessary. Various safety mechanisms are implemented to safeguard the patient from erratic controller actions caused by external disturbances. This system completed with a friendly interface has shown satisfactory performance in estimating and controlling the depth of anesthesia.

Anesthesia control using midlatency auditory evoked potentials

This paper shows the development of a system to control inhalation anesthetic concentration delivered to a patient based upon that patient's midlatency auditory evoked potentials (MLAEP's). It was developed and tested in dogs by determining response to the supramaximal stimulus of tail clamping. Prior to tail clamp, the MLAEP was recorded along with inhalational anesthetic concentration and classified as responders or nonresponders as determined by tail clamping. This was performed at a number of different anesthetic levels to obtain a data training set. The MLAEP's were compacted by means of discrete time wavelet transform (DTWT), and together with anesthetic concentration value, a stepwise discriminant analysis (SDA) was performed to determine those features which could separate responders from nonresponders. It was determined that only three features were necessary for this recognition. These features were then used to train a four-layer artificial neural network (ANN) to separate the responders from nonresponders. The network was tested using a separate set of data, resulting in a 93% recognition rate in the anesthetic transition zone between responders and nonresponders, and 100% recognition rate outside this zone. The anesthetic controller used this ANN combined with fuzzy logic and rule-based control. A set of ten animal experiments were performed to test the robustness of this controller. Acceptable clinical performance was obtained, showing the feasibility of this approach.

Multiple-drug hemodynamic control using fuzzy decision theory

A fuzzy-logic-based, automated drug-delivery system has been developed and validated on a nonlinear canine circulatory model for managing hemodynamic states. This controller features: 1) a fuzzy decision analysis module for patient status determination by assessing cardiac index, systemic vascular resistance index, and pulmonary vascular resistance index and 2) a fuzzy hemodynamic management module utilizing dopamine, phenylephrine, nitroprusside, and nitroglycerin for regulating mean arterial pressure, mean pulmonary arterial pressure, and cardiac output. A rule-based drug delivery scheduling program has been devised and incorporated to execute the therapeutic strategy as recommended by the decision analysis module. Compared to the existing controllers, this system is able to achieve a faster response time with a more secured and effective regulation. The simulation results have demonstrated the feasibility of the decision analysis process for automated management of the arterial and venous circulation with an expanded arsenal of pharmacological agents.

Design of a recognition system to predict movement during anesthesia

The need for a reliable method of predicting movement during anesthesia has existed since the introduction of anesthesia. This paper proposes a recognition system, based on the autoregressive (AR) modeling and neural network analysis of the electroencephalograph (EEG) signals, to predict movement following surgical stimulation. The input to the neural network will be the AR parameters, the hemodynamic parameters blood pressure (BP) and heart rate (HR), and the anesthetic concentration in terms of the minimum alveolar concentration (MAC). The output will be the prediction of movement. Design of the system and results from the preliminary tests on dogs are presented in this paper. The experiments were carried out on 13 dogs at different levels of halothane. Movement prediction was tested by monitoring the response to tail clamping, which is considered to be a supramaximal stimulus in dogs. The EEG data obtained prior to tail clamping was processed using a tenth-order AR model and the parameters obtained were used as input to a three-layer perceptron feedforward neural network. Using only AR parameters the network was able to correctly classify subsequent movement in 85% of the cases as compared to 65% when only hemodynamic parameters were used as the input to the network. When both the measures were combined, the recognition rate rose to greater than 92%. When the anesthetic concentration was added as an input the network could be considerably simplified without sacrificing classification accuracy. This recognition system shows the feasibility of using the EEG signals for movement during anesthesia.

Issues in the design of a multirate model-based controller for a nonlinear drug infusion system

Multivariable controller design for the regulation of mean arterial pressure (MAP) and cardiac output (CO) in congestive heart failure patients is restricted by the limited frequency of CO sampling. Performance criteria for the controller specify maximum allowable transient settling times for both variables, and the design should account for the inherent multirate nature of the process in order to satisfy these criteria. We present a multirate model predictive control (MPC) design for MAP and CO regulation by combined infusion of sodium nitroprusside and dopamine, based on a comprehensive nonlinear model of the system. The multirate MPC algorithm is based on nonlinear quadratic dynamic matrix control. To reduce computation time, we introduce a selective linearization technique that linearizes the model on the basis of trends in the plant-model mismatch. The problem is complicated by restrictions on initial dopamine infusion, prescribed to avoid extremely slow responses. We present a novel rule-based override (RBO) to the MPC controller that uses a set of heuristics to initialize dopamine. The performance of the MPC/RBO controller is illustrated using simulation results.

Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model

A control device that uses an expert system approach for a two input-two output system has been developed and evaluated using a mathematical model of the hemodynamic response of a dog. The two inputs are the infusion rates of two drugs: sodium nitroprusside (SNP) and dopamine (DPM). The two controlled variables are the mean arterial pressure and the cardiac output. The control structure is dual mode, i.e., it has two levels: a critical conditions (coarse) control mode and a noncritical conditions (fine) control mode. The system switches from one to the other when threshold conditions are met. Different "controller parameters sets"-including the values for the threshold conditions-can be given to the system which will lead to different controller outputs. Both control modes are rule-based, and supervisory capabilities are added to ensure adequate drug delivery. The noncritical control mode is a fuzzy logic controller. The system includes heuristic features typically considered by anesthesiologists, like waiting periods and the observance of a "forbidden dosage range" for DPM infusion when used as an inotrope. An adaptation algorithm copes with the wide range of sensitivities to SNP found among different individuals, as well as the time varying sensitivity frequently observed in a single patient. The control device is eventually tested on a nonlinear model, designed to mimic the conditions of congestive heart failure in a dog. The test runs show a highest overshoot of 3 mmHg with nominal SNP sensitivity. When tested with different simulated SNP sensitivities, the controller adaptation produces a faster response to lower sensitivities, and reduced oscillations to higher sensitivities. The simulations seem to show that the system is able to drive and adequately keep the two hemodynamic variables within prescribed limits.

The rat growth hormone proximal silencer contains a novel DNA-binding site for multiple nuclear proteins that represses basal promoter activity

Cell-type-specific expression of the rat growth hormone (rGH) gene is determined by the interaction of both positive as well as negative regulatory proteins with cis-acting elements located upstream of the rGH mRNA start site. We have recently shown that the rat liver transcription factor NF1-L binds to the proximal rGH silencer (called silencer-1) to repress its transcriptional activity. However, this single factor proved to be insufficient by itself to confer cell-specific gene repression. We therefore attempted to identify other regulatory proteins interacting with silencer 1, which might be needed to achieve full cell-specific repression of that gene. A common recognition site for three yet uncharacterized nuclear proteins (designated as SBP1, SBP2 and SBP3) which bind a DNA sequence adjacent to the NF1-L-binding site in the rGH silencer-1 element were identified. UV crosslinking of DNA/protein complexes and nuclear protein fractionation/renaturation from SDS/polyacrylamide gels further indicated that the molecular masses for SBP1-3 are 41, 26 and 17 kDa respectively, the major species being the 26-kDa protein (SBP2) which account for 83% of the shifted SBP double-stranded oligonucleotide in gel mobility-shift assays. For this reason, most of this study focussed on the characterization of SBP2. We demonstrated that binding of NF1-L and SBP2 to their respective recognition sequence is a mutually exclusive event. Although an SBP-binding activity has been found in every non-pituitary tissue or cell line tested, no such activity could be detected in either rat pituitaries or rat pituitary GH4C1 cells. Insertion of the SBP element upstream of the basal promoter of the mouse p12 heterologous gene resulted in a consistent decrease in chloramphenicol acetyl transferase reporter gene expression following transient transfections in non-pituitary cells only, suggesting that the related SBP1-3 proteins might be involved in generally repressing gene transcription in a cell-specific manner.

Time-frequency spectral representation of the EEG as an aid in the detection of depth of anesthesia

A time-frequency spectral representation (TFSR) has been used to study the nonstationary information in the EEG as an aid in determining the anesthetic depth. This paper uses a TFSR with an exponential weighting function for the purpose. Raw EEG data were collected form 10 mongrel dogs at various levels of halothane anesthesia. Depth of anesthesia was tested by observing the response to tail clamping, which is considered a supramaximal stimulus in dogs. A positive response was graded as awake (depth 0), and a negative response was graded as asleep (depth 1). The EEG obtained during a period of 30 sec tail clamp was processed into TFSRs. It was observed that at depth 0, the spectrum becomes localized in time and frequency. The percentage of energy in the delta (1-3.5 Hz) and theta (3.5-7.5 Hz) frequency bands increased. At depth 1, the spectrum remained unchanged throughout the period of tail clamp. The performance of the TFSR in detecting the patient's awareness was also compared with the power spectrum. It was concluded that under certain anesthetic conditions, the TFSR is superior to the power spectrum.

The 30-kDa rat liver transcription factor nuclear factor 1 binds the rat growth-hormone proximal silencer

Transcription of the gene encoding rat growth hormone is under the influence of cis-acting negative regulatory elements termed silencers. We showed previously that one such element, designated the rat growth hormone proximal silencer-1 site, binds a nuclear protein, the nuclear-factor-1-like protein that is probably a member of the CAAT transcription factor/nuclear-factor-1 (CTF/NF-I) family of transcription factors. This nuclear protein possesses DNA-binding activity as well as biochemical properties similar to those reported for the 30-kDa rat liver form of nuclear factor 1 (NF1-L). Results from both gel mobility supershift assays and Western-blot analyses, performed in combination with a polyclonal antibody directed against the DNA-binding domain of NF1-L, indicated that rat liver nuclear factor 1 might indeed correspond to one of the transcription factors interacting with the rat growth-hormone proximal silencer element. Further experiments using gel mobility shift assays also indicated that, as for NF1-L, multiple proteins among the 52-66-kDa CTF/NF-I isoforms from human HeLa cells also possess the ability to bind the rat growth-hormone silencer.

The US-1 element from the gene encoding rat poly(ADP-ribose) polymerase binds the transcription factor Sp1

By comparing the upstream DNA sequence of the rat and human genes encoding poly(ADP-ribose) polymerase (PARP), we have defined a 16-bp conserved region and designated it as US-1 for 'upstream sequence 1'. This element is homologous to the recently described binding site for the transcription factor Sp1 in the promoter sequence of the mouse p12 gene which encodes a protease inhibitor. Analyses in gel mobility shift assays revealed that a nuclear protein, produced by all tissue-culture cells tested, specifically binds the US-1 element. The pattern of shifted DNA protein complexes obtained was strikingly similar to that for Sp1, which is supported by the positive displacement of these complexes by an oligomer containing the Sp1 binding site in gel shift competition experiments. Replacement of the Sp1 binding site from the basal promoter of the mouse p12 gene by the rPARP US-1 element did not result in any significant variations in the level of expression of the chloramphenicol acetyltransferase (CAT) reporter gene upon transient transfection of tissue-culture cells. However, when point mutations are introduced in the US-1 element in a similar substitution experiment, a significant reduction in CAT gene expression could be observed. These data are consistent with Sp1 interacting with the US1 element. Results from DNase I footprinting experiments clearly indicated that purified Sp1 not only binds to the US-1 element but also to four other closely located cis-acting sites scattered in the promoter of the rat PARP gene, therefore suggesting that Sp1 is likely to modulate strongly the expression of that gene in different tissues.

Expression of the rat growth-hormone gene is under the influence of a cell-type-specific silencer element

We have previously shown that a cell-type-specific negative-regulatory element, or silencer, acts to specifically restrict rat-growth-hormone(rGH)-promoter activity to pituitary cells. Here we report a detailed characterization of this element. The activity of the silencer is dependent on its position relative to the promoter. The negative regulatory effect can be diminished by cotransfection with a high-copy-number, silencer-containing competitor plasmid, suggesting that the function of the element is mediated by specific binding of a trans-acting negative-regulatory factor. The minimal region required for silencer function is contained between positions -309 and -266 relative to the start of the rGH mRNA. The specific interaction of a nuclear protein from non-pituitary cells with this rGH DNA segment was shown by DNaseI as well as dimethylsulfate methylation-interference footprinting. A detailed examination of the DNA-binding site for that protein clearly suggest that it belongs to the NF1 family of transcription factors.

An adaptive controller for the administration of closed-circuit anesthesia during spontaneous and assisted ventilation

Although reduced waste of expensive anesthetic gases is a strong incentive to use closed-circuit anesthesia, manual methods of performing closed-circuit anesthesia are labor intensive and thus not widely used. Automation of closed-circuit anesthesia delivery may reduce the work. A pressure-based adaptive controller was designed and tested on mongrel dogs to evaluate the feasibility of automating closed-circuit anesthesia using an accessory to an existing clinical anesthesia machine and a gas analyzer. The controller was found stable and responsive with good control of oxygen concentration and acceptable control of halothane end-tidal concentration. The response time for oxygen was 5.23 +/- 1.26 minutes, and that for halothane was 2.67 +/- 1.83 minutes. The average peak overshoot for halothane at the start of the experiment was 26.9%. This pressure controller differs from previously published closed-circuit anesthesia controllers that measure gas volume changes within a mechanical ventilator. A pressure-based controller is easily attached to a standard anesthesia machine and is compatible with modes of ventilation other than controlled mechanical ventilation. The controller used in this study is not designed for clinical use, but was developed to investigate the feasibility of pressure as a basis for gas volume control in closed-circuit anesthesia administration.

Adaptive control of multiplexed closed-circuit anesthesia

This paper describes the design of an adaptive closed-circuit anesthesia controller based on a multiplexed mass spectrometer system. The controller deals with measurement deterioration caused by measurement delay and rise time through a long catheter as well as long sampling times due to the multiplexed measurements. Measurement data are extrapolated between sampling periods to increase the estimation convergence rate. A multiple-step-ahead predictive control algorithm is used to calculate intermediate control inputs between sampling intervals. Simulations are used to validate the designed controller.

Multiple-model adaptive predictive control of mean arterial pressure and cardiac output

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.

Binding of a nuclear protein to the rat growth hormone silencer element

The rat growth hormone (rGH) gene is uniquely expressed in a subset of cells from the anterior pituitary. This strongly cell type specific expression is controlled by both cis-acting positive sequences that bind the pituitary specific transcription factor Pit-1 and cis-acting negative regulatory elements that lie upstream of the Pit-1 sites. The negative elements act to prevent expression of the gene in inappropriate cell types. Here we report that the most proximal rGH silencer element is specifically bound by a protein found in a number of rGH non-expressing cell types and which exerts a negative regulatory effect through the recognition of this rGH element in transient transfection assays. The sequence recognized by this protein is similar to sequences of several other negative regulatory elements as well as to the consensus binding site for the transcription factor NF1. However, the 45 KDa molecular weight identified for this protein does not correspond to any of the sizes previously reported for NF1 suggesting that it is likely to represent a new member amongst this family of transcription factors.

A short protocol for micro-purification of nuclear proteins from whole animal tissue

Although a number of small-scale procedures have been described for the preparation of crude nuclear extracts from established cell lines, none were provided for the preparation of similar extracts from small amounts of animal tissue. In addition, no small-scale procedures contain enrichment steps that render the detection of low-abundant DNA-binding proteins easier. Here we describe a simple, efficient procedure for the rapid preparation of high-quality nuclear extracts from either whole animal tissue or established cell lines. It is based on a rapid isolation of the nuclei followed by a KCl extraction and a further micro-enrichment of the DNA binding proteins on heparin Sepharose CL-6B. Extracts prepared in such a way are suitable for the analysis of specific DNA/protein interactions by the use of gel shift assays or by DNaseI and dimethylsulfate footprinting techniques. Most importantly, the entire process can be fulfilled at minimal cost within a day on as little as one gram of fresh tissue, which renders this procedure extremely attractive for the analysis of DNA binding proteins involved in the control of gene expression.

Modeling the hemodynamic response to dopamine in acute heart failure

A descriptive incremental nonlinear single-input-multiple-output (SIMO) model of the hemodynamic response [cardiac output (CO) and mean aortic pressure (MAP)] to the inotropic drug dopamine in acute ischemic heart failure was constructed to facilitate the design of closed-loop control systems. The structure of the CO component of the model is a first-order system with a sigmoidal relationship. The MAP component is a first-order system with a threshold. Parameter identification was performed on data collected during positive step (drug on) and negative step (drug off) testing using multiple levels (2-6 mcg/kg/min) of infusion of dopamine in a canine model of acute ischemic heart failure. Parameter estimation utilized a least squares objective function and a linearized form of the step response of the model in the time domain. The model provides good approximations to the mean empirical responses.

Adaptive control of closed-circuit anesthesia

Closed-circuit anesthesia (CCA) is more economical and ecologically safer than open circuit anesthesia. However, gas concentrations are more difficult to control. Computer control of CCA has been proposed to facilitate its use. Past efforts have either been limited to the control of anesthetic gas concentrations or apply only to a small group of patients. This paper describes a comprehensive control system applicable to a large class of patients. This system controls the end-tidal oxygen and anesthetic gas concentrations, and the circuit volume. The CCA process was modeled by writing mass balance equations. Simplifying assumptions yielded a bilinear single-input-single-output model for the anesthetic gas concentration and a bilinear multiple-input-multiple-output model for the circuit volume and oxygen concentration. One-step-ahead controllers were used to control these two subsystems. Simulations showed that the control performance was most sensitive to the gas uptakes. Three independent, least-mean-squares estimation schemes were implemented to estimate the uptakes of oxygen, nitrous oxide, and anesthetic gas. These estimates were used in the control law and resulted in explicit adaptive control. The performance of the adaptive controller was compared to that of a fixed controller (with precalculated gas uptakes) in five animal experiments. The adaptive controller performed better than the fixed controller in all cases. The most significant difference was in the anesthetic gas response time 3.6 +/- 0.70 min for adaptive control and 7.04 +/- 5.62 min for fixed control. The adaptive controller was also robust with respect to variations in the system parameters such as the functional residual capacity, leak, deadspace and gas uptakes.(ABSTRACT TRUNCATED AT 250 WORDS)

A circulatory model for combined nitroprusside-dopamine therapy in acute heart failure

A computer model was developed to approximate the hemodynamic responses of dopamine and nitroprusside in acute left ventricular pump failure. The model is intended to aid the design of a multiple drug infusion system. A non-linear electrical analog model with baroreflex feedback was used to simulate the circulatory system. Heart failure was represented by a reduction in left ventricular inotropy. Pharmacodynamic relationships between the drugs studied and several elements of the system were incorporated into the model to simulate the overall drug responses which include secondary interactions between vascular components. Despite several shortcomings, the model showed good agreement with experimental and clinical data.

A feedback controller for ventilatory therapy

A computerized system that uses feedback of end-tidal CO2 fraction (FETCO2) to adjust minute volume of a ventilator has been developed and tested. The effectiveness and robustness of the controller were evaluated in five anesthetized dogs. The controller responded to step-changes in the set-point for FETCO2 by adjusting minute volume so that the FETCO2 settled to the new set-point in less than 60 sec with less than 20% overshoot. The system exhibited suitable dynamic response to step-changes in set-point with loop gains as large as two times and as small as one-half the optimal value. The breath-to-breath variation in FETCO2 values during prolonged periods of closed-loop controlled ventilation was smaller than the variation during periods of constant minute volume ventilation in three of five experiments. The controller generally maintained FETCO2 within +/- 0.1 vol% of the set-point. A disturbance to the controlled system was produced by releasing an occlusion of a branch of the pulmonary artery. The controller always responded to this disturbance in a stable manner, returning the FETCO2 to its desired value within 30 sec. Accurate control of arterial partial pressure of CO2(PaCO2) will require modifications enabling the system to determine the relationship between FETCO2 and PaCO2.

A possible role of prostaglandin F-2 alpha in the development of ovarian follicles in guinea-pigs

Uterine PGF-2 alpha in the guinea-pig was eliminated by hysterectomy on Day 9 of the oestrous cycle and an increasing period of reduced PGF-2 alpha availability to the ovary was created by deferring replacement therapy with PGF-2 alpha from Day 9 to Day 11, 13 and 15. Ovulation, observed in 94% of the animals, was inevitably delayed. The treatment cycle length was progressively extended from the normal 15--16 days to 19--21 days. Similar results were obtained if endogenous PGF-2 alpha was suppressed by indomethacin injection instead of removing the uterus. That ovulation had been delayed and not occurred from more recently recruited follicles was evident from the total absence of atretic follicles of the expected size had they become atretic due to the lack of PGF-2 alpha at some critical time between Days 9 and 15. Pituitary homogenate and hCG injected on Day 16 shortened the PGF-2 alpha treatment cycle by 2 days, but stimulated ovulation in only 42% of the animals. When endogenous PGF-2 alpha was eliminated by hysterectomy and daily injections of indomethacin, follicular development ceased at approximately 0.8 mm instead of the 1.0 mm diameter found just before the preovulatory LH surge. It is suggested that (1) PGF-2 alpha has to act on developing follicles for a given period before they are capable of ovulation, and (2) that PGF-2 alpha is one of the factors required to stimulate the growth of the Graafian follicles at the end of the oestrous cycle.