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Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods-SAPV direct coupling and Maximum Power Point Tracking control-in optimizing energy harvesting from solar panels., (© 2024. The Author(s).)

A novel property existing in the stain-etching technique that eliminates the need for expensive etchant masks in the texturization process of silicon wafers was identified. Through the combination of grayscale lithography and stain-etching methodologies, selective patterning of silicon with AR-P 3510 T, a positive-photoresist mask, was carried out. The etch area ratio was varied in nine different patterns of various feature sizes ranging from 400 to 1500 μm. The optical characteristics of the patterned substrates were determined from diffuse reflectance spectroscopy analysis, and the results were supported with finite-difference time-domain simulations. Complimenting the improvement in optical properties, the electrical losses in microwell-patterned photodetector devices have been reduced with an electro-optic optimum value of the surface enhancement factor, γ. The photodetecting efficiency of a selectively patterned microwell photodetector device exceeded the planar and black silicon photodetector devices with a considerable improvement in the pyro-phototronic effect. This work suggests an alternative for black silicon optoelectronic devices providing a new route to fabricate selectively patterned substrates with an achieved detectivity 16- and 20-fold higher than black and planar silicon photodetector devices, respectively.

Background: Supported by laboratory and clinical investigations of adult cardiopulmonary arrest, resuscitation guidelines recommend monitoring end-tidal carbon dioxide (ETCO 2 ) as an indicator of cardiopulmonary resuscitation (CPR) quality, but they note that "specific values to guide therapy have not been established in children.", Methods: This prospective observational cohort study was a National Heart, Lung, and Blood Institute-funded ancillary study of children in the ICU-RESUS trial (Intensive Care Unit-Resuscitation Project; NCT02837497). Hospitalized children (≤18 years of age and ≥37 weeks postgestational age) who received chest compressions of any duration for cardiopulmonary arrest, had an endotracheal or tracheostomy tube at the start of CPR, and evaluable intra-arrest ETCO 2 data were included. The primary exposure was event-level average ETCO 2 during the first 10 minutes of CPR (dichotomized as ≥20 mm Hg versus <20 mm Hg on the basis of adult literature). The primary outcome was survival to hospital discharge. Secondary outcomes were sustained return of spontaneous circulation, survival to discharge with favorable neurological outcome, and new morbidity among survivors. Poisson regression measured associations between ETCO 2 and outcomes as well as the association between ETCO 2 and other CPR characteristics: (1) invasively measured systolic and diastolic blood pressures, and (2) CPR quality and chest compression mechanics metrics (ie, time to CPR start; chest compression rate, depth, and fraction; ventilation rate)., Results: Among 234 included patients, 133 (57%) had an event-level average ETCO 2 ≥20 mm Hg. After controlling for a priori covariates, average ETCO 2 ≥20 mm Hg was associated with a higher incidence of survival to hospital discharge (86/133 [65%] versus 48/101 [48%]; adjusted relative risk, 1.33 [95% CI, 1.04-1.69]; P =0.023) and return of spontaneous circulation (95/133 [71%] versus 59/101 [58%]; adjusted relative risk, 1.22 [95% CI, 1.00-1.49]; P =0.046) compared with lower values. ETCO 2 ≥20 mm Hg was not associated with survival with favorable neurological outcome or new morbidity among survivors. Average 2 ≥20 mm Hg was associated with higher systolic and diastolic blood pressures during CPR, lower CPR ventilation rates, and briefer pre-CPR arrest durations compared with lower values. Chest compression rate, depth, and fraction did not differ between ETCO 2 groups., Conclusions: In this multicenter study of children with in-hospital cardiopulmonary arrest, ETCO 2 ≥20 mm Hg was associated with better outcomes and higher intra-arrest blood pressures, but not with chest compression quality metrics., Competing Interests: Disclosures None.

Polynomial expansions are important in the analysis of neural network nonlinearities. They have been applied thereto addressing well-known difficulties in verification, explainability, and security. Existing approaches span classical Taylor and Chebyshev methods, asymptotics, and many numerical approaches. We find that, while these have useful properties individually, such as exact error formulas, adjustable domain, and robustness to undefined derivatives, there are no approaches that provide a consistent method, yielding an expansion with all these properties. To address this, we develop an analytically modified integral transform expansion (AMITE), a novel expansion via integral transforms modified using derived criteria for convergence. We show the general expansion and then demonstrate an application for two popular activation functions: hyperbolic tangent and rectified linear units. Compared with existing expansions (i.e., Chebyshev, Taylor, and numerical) employed to this end, AMITE is the first to provide six previously mutually exclusive desired expansion properties, such as exact formulas for the coefficients and exact expansion errors. We demonstrate the effectiveness of AMITE in two case studies. First, a multivariate polynomial form is efficiently extracted from a single hidden layer black-box multilayer perceptron (MLP) to facilitate equivalence testing from noisy stimulus-response pairs. Second, a variety of feedforward neural network (FFNN) architectures having between three and seven layers are range bounded using Taylor models improved by the AMITE polynomials and error formulas. AMITE presents a new dimension of expansion methods suitable for the analysis/approximation of nonlinearities in neural networks, opening new directions and opportunities for the theoretical analysis and systematic testing of neural networks.

Objective: Parkinson disease (PD) is a progressive neurodegenerative disorder with an annual incidence of approximately 0.1%. While primarily considered a motor disorder, increasing emphasis is being placed on its non-motor features. Both manifestations of the disease affect quality of life (QoL), which is captured in part II of the Unified Parkinson's Disease Rating Scale (UPDRS-II). While useful in the management of patients, it remains challenging to predict how QoL will change over time in PD. The goal of this work is to explore the feasibility of a machine learning algorithm to predict QoL changes in PD patients., Methods: In this retrospective cohort study, patients with at least 12 months of follow-up were identified from the Parkinson's Progression Markers Initiative database (N = 630) and divided into two groups: those with and without clinically significant worsening in UPDRS-II (n = 404 and n = 226, respectively). We developed an artificial neural network using only UPDRS-II scores, to predict whether a patient would clinically worsen or not at 12 months from follow-up., Results: Using UPDRS-II at baseline, at 2 months, and at 4 months, the algorithm achieved 90% specificity and 56% sensitivity., Interpretation: A learning model has the potential to rule in patients who may exhibit clinically significant worsening in QoL at 12 months. These patients may require further testing and increased focus., (© 2022 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic. We are especially interested in two current topics: effect of vaccination and the universally observed oscillations in infections. We use a nonlinear Susceptible, Infected, & Immune model incorporating a dynamic transmission rate and vaccination policy. The US data provides a starting point for analyzing stability, bifurcations and dynamics in general. Further parametric analysis reveals a saddle-node bifurcation under imperfect vaccination leading to the occurrence of sustained epidemic equilibria. This work points to the tremendous value of systematic nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of vaccination, and frequency, phase, and amplitude of transmission rate on the persistent dynamic behavior of the disease., Competing Interests: Conflict of interestThe authors declare that they have no conflicts of interest., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Overhung rotors usually exhibit recurrent transitions through critical whirl rotational speeds during startup and coast down operations, which significantly differ from their steady-state whirl responses. The presence of angular acceleration results in a linear-time-varying (LTV) system, which, although technically linear, still presents complexities often evinced by a nonlinear system. In general, backward whirl zones can either precede the critical forward whirl speed (termed as pre-resonance backward whirl, Pr-BW), or immediately follow the critical forward whirl speed (termed as post-resonance backward whirl, Po-BW). The Po-BW in the whirl response of a cracked overhung rotor with a breathing crack is studied here as distinct from that of geometrically symmetric configurations of other rotor systems. The equations of motion from the finite element (FE) model of an overhung rotor system with a breathing crack are numerically integrated to obtain the whirl response. The transient whirl responses with different bearing conditions are thoroughly investigated for excitation of Po-BW. The Po-BW zones of rotational speeds are determined via the wavelet transform method and full spectrum analysis (FSA) and applied to signals with added noise. The results of this work confirm the excitation of the Po-BW in cracked overhung rotors and confirm the robustness of the employed methods., (© 2022. The Author(s).)

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) in neonates after heart surgery. Our prior work shows that the Support Vector Machine (SVM) classifier can be a powerful tool in predicting clinical outcomes of such complicated and uncommon diseases, even when the number of data samples is low. In the presented work, we first illustrate and discuss the shortcomings of the traditional automatic machine learning (aML) approach. Consequently, we describe our methodology for addressing these shortcomings, while utilizing the designed interactive ML (iML) algorithm. Finally, we conclude with a discussion of the developed method and the results obtained. In sum, by adding an additional (Genetic Algorithm) optimization step in the SVM learning framework, we were able to (a) reduce the dimensionality of an SVM model from 248 to 53 features, (b) increase generalization that was confirmed by a 100% accuracy assessed on an unseen testing set, and (c) improve the overall SVM model's performance from 65% to 100% testing accuracy, utilizing the proposed iML method., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Objectives: To design, implement, and evaluate a simulation-based education (SBE) program for caregivers of children with tracheostomy., Methods: Self-reported comfort and confidence in knowledge as well as tracheostomy care skills were assessed before and after a single SBE session for 24 consecutively enrolled caregivers of children with tracheostomies aged <21 years who were hospitalized at an academic medical center from August 2018 to September 2019 by using a survey and checklist, respectively. Mean individual and aggregated scores were compared by using a paired samples t-test, and association between instruments was determined with Spearman correlation., Results: Post-SBE, there was a significant improvement in both self-reported comfort and confidence ( P < .001) and checklist assessment of most tracheostomy care skills ( P < .001). There were no significant correlations between caregivers' self-reported comfort and confidence and skills pre-SBE (ρ = 0.13) or post-SBE (ρ = 0.14). Cronbach's α coefficients for the survey ranged from 0.93 to 0.95 and for the checklist from 0.58 to 0.67. Seventeen percent of caregivers competently completed the entire checklist post-SBE, with most caregivers missing 1 or 2 critical skills such as obturator removal after tracheostomy insertion., Conclusions: In this pilot study, we demonstrated successful design and implementation of an SBE program for caregivers of children with tracheostomies, revealing improvements in self-reported comfort and confidence as well as in their performance of tracheostomy care skills. Further optimization is needed, and caregivers may benefit from additional SBE sessions to achieve complete skills competency. Future research on the long-term impact of SBE and the peer-to-peer support element of the program is needed., Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose., (Copyright © 2021 by the American Academy of Pediatrics.)

Introduction/hypothesis: The outcome of cardiopulmonary resuscitation (CPR) depends on timely recognition of the underlying cause of cardiac arrest. Ventricular fibrillation (VF) waveform analysis to differentiate primary VF from secondary asphyxia-associated VF may allow tailoring of therapies to improve cardiac arrest outcomes. Therefore, the primary goal of this investigation was to develop a novel technique utilizing wavelet synchrosqueezed transform (WSST) and decision-tree classifier that was specifically adapted to discriminate between these two incidents of VF., Methods: Secondary analytical investigation of electrocardiography (ECG) data obtained from swine models of either primary VF (n=18) or secondary asphyxia-associated VF (7min of asphyxia prior to VF induction; n=12). In the primary analysis, WSST technique was applied to the first 35s of the VF ECG signal to identify the most differentiating characteristics of the signal for use as features to develop a machine learning algorithm to classify the arrest as either primary VF vs. secondary asphyxia-associated VF. The performance of this new interactive Machine Learning algorithm with Wavelet Energy features of ECG (MLWAVE) was assessed using both classification accuracy and area under the receiver operating characteristic curve (AUCROC). To evaluate the validity of the new technique, the amplitude spectrum area (AMSA)-based technique, a well-established defibrillation classification method, was also applied to the same ECG signals. The classification accuracy and AUCROC were then compared between the two techniques., Results: For the primary analysis evaluating the first 35s of the VF waveform, the MLWAVE technique classified the type of VF with high accuracy (28/28 [100%], AUCROC: 1.00). The MLWAVE technique performed better than the AMSA technique across all comparisons, but given the small sample sizes, differences were not statistically significant (accuracy: 100% vs. 85.7%; p=0.24; AUCROC: 1.00 vs. 0.82; p=0.24)., Conclusion: This analytical investigation illustrates the advantages of the MLWAVE signal processing method which was associated with 100% accuracy in classifying the type of VF waveform: primary vs. asphyxia-associated. Such classification could lead to personalized tailoring of resuscitation (e.g., immediate defibrillation vs. continued CPR and treatment of reversible cardiac arrest causes before defibrillation) to improve outcomes for cardiac arrest.

Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully and to use that as a basis to explore the nonlinear behavior. We use a nonlinear susceptible, exposed, infectious and removed transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID-19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© Springer Nature B.V. 2020.)

Introduction: The quality of cardiopulmonary resuscitation (CPR) has been shown to impact patient outcomes. However, post-CPR morbidity and mortality remain high, and CPR optimization is an area of active research. One approach to optimizing CPR involves establishing reliable CPR performance measures and then modifying CPR parameters, such as compressions and ventilator breaths, to enhance these measures. We aimed to define a reliable CPR performance measure, optimize the CPR performance based on the defined measure and design a dynamically optimized scheme that varies CPR parameters to optimize CPR performance., Materials and Methods: We selected total blood gas delivery (systemic oxygen delivery and carbon dioxide delivery to the lungs) as an objective function for maximization. CPR parameters were divided into three categories: rescuer dependent, patient dependent, and constant parameters. Two optimization schemes were developed using simulated annealing method: a global optimization scheme and a sequential optimization scheme., Results and Discussion: Variations of CPR parameters over CPR sequences (cycles) were analyzed. Across all patient groups, the sequential optimization scheme resulted in significant enhancement in the effectiveness of the CPR procedure when compared to the global optimization scheme., Conclusions: Our study illustrates the potential benefit of considering dynamic changes in rescuer-dependent parameters during CPR in order to improve performance. The advantage of the sequential optimization technique stemmed from its dynamically adapting effect. Our CPR optimization findings suggest that as CPR progresses, the compression to ventilation ratio should decrease, and the sequential optimization technique can potentially improve CPR performance. Validation in vivo is needed before implementing these changes in actual practice.

Periventricular leukomalacia (PVL) is brain injury that develops commonly in neonates after cardiac surgery. Earlier identification of patients who are at higher risk for PVL may improve clinicians' ability to optimize care for these challenging patients. The aim of this study was to apply machine learning algorithms and wavelet analysis to vital sign and laboratory data obtained from neonates immediately after cardiac surgery to predict PVL occurrence. We analyzed physiological data of patients with and without hypoplastic left heart syndrome (HLHS) during the first 12 h after cardiac surgery. Wavelet transform was applied to extract time-frequency information from the data. We ranked the extracted features to select the most discriminative features, and the support vector machine with radial basis function as a kernel was selected as the classifier. The classifier was optimized via three methods: (1) mutual information, (2) modified mutual information considering the reliability of features, and (3) modified mutual information with reliability index and maximizing set's mutual information. We assessed the accuracy of the classifier at each time point. A total of 71 neonates met the study criteria. The rates of PVL occurrence were 33% for all patients, with 41% in the HLHS group and 25% in the non-HLHS group. The F-score results for HLHS patients and non-HLHS patients were 0.88 and 1.00, respectively. Using maximizing set's mutual information improved the classifier performance in the all patient groups from 0.69 to 0.81. The novel application of a modified mutual information ranking system with the reliability index in a PVL prediction model provided highly accurate identification. This tool is a promising step for improving the care of neonates who are at higher risk for developing PVL following cardiac surgery.

Cardiopulmonary resuscitation (CPR) is used widely to rescue cardiac arrest patients, yet some physiological aspects of the procedure remain poorly understood. We conducted this study to characterize the dynamic mechanical properties of the thorax during CPR in a swine model. This is an important step toward determining optimal CPR chest compression mechanics with the goals of improving the fidelity of CPR simulation manikins and ideally chest compression delivery in real-life resuscitations. This paper presents a novel nonlinear model of the thorax that captures the complex behavior of the chest during CPR. The proposed model consists of nonlinear elasticity and damping properties along with frequency dependent hysteresis. An optimization technique was used to estimate the model coefficients for force-compression using data collected from experiments conducted on swine. To track clinically relevant, time-dependent changes of the chest's properties, the data was divided into two time periods, from 1 to 10 min (early) and greater than 10 min (late) after starting CPR. The results showed excellent agreement between the actual and the estimated forces, and energy dissipation due to viscous damping in the late stages of CPR was higher when compared to the earlier stages. These findings provide insight into improving chest compression mechanics during CPR, and may provide the basis for developing CPR simulation manikins that more accurately represent the complex real world changes that occur in the chest during CPR.

This paper is concerned with the mathematical modeling and detection of endotracheal (ET) intubation in children under general anesthesia during surgery. In major pediatric surgeries, the airway is often secured with an endotracheal tube (ETT) followed by initiation of mechanical ventilation. Clinicians utilize auscultation of breath sounds and capnography to verify correct ETT placement. However, anesthesia providers often delay timely charting of ET intubation. This latency in event documentation results in decreased efficacy of clinical decision support systems. In order to target this problem, we collected real inpatient data and designed an algorithm to accurately detect the intubation time within the clinically valid range; the results show that we are able to achieve high accuracy in more than 96% of the cases. Automatic detection of ET intubation time would thus enhance better real-time data capture to support future improvement in clinical decision support systems.

In this paper we present a new expert knowledge based clinical decision support system for prediction of intensive care units outcome based on the physiological measurements collected during the first 48 hours of the patient's admission to the ICU. The developed CDSS algorithm is composed of several stages. First, we categorize the collected data based on the physiological organ that they represent. We then extract clinically relevant features from each data category and then rank these features based on their mutual information with the outcome. Then, we design an artificial neural network to serve as a classifier to detect patients at high risk of critical deterioration. We use the eight-fold cross validation method to test the developed CDSS classifier. The results from the classification show that the newly designed CDSS outperforms the widely used acuity scoring systems, SOFA and SAPS-III. The F-score classification result of our developed algorithms is 42% while the F-score results for SOFA and SAPS-III are 26% and 29% respectively.

This paper is concerned with the mathematical modeling of a severe and common congenital defect called hypoplastic left heart syndrome (HLHS). Surgical approaches are utilized for palliating this heart condition; however, a brain white matter injury called periventricular leukomalacia (PVL) occurs with high prevalence at or around the time of surgery, the exact cause of which is not known presently. Our main goal in this paper is to study the hemodynamic conditions under which HLHS physiology may lead to the occurrence of PVL. A lumped parameter model of the HLHS circulation has been developed integrating diffusion modeling of oxygen and carbon dioxide concentrations in order to study hemodynamic variables such as pressure, flow, and blood gas concentration. Results presented include calculations of blood pressures and flow rates in different parts of the circulation. Simulations also show changes in the ratio of pulmonary to systemic blood flow rates when the sizes of the patent ductus arteriosus and atrial septal defect are varied. These changes lead to unbalanced blood circulations and, when combined with low oxygen and carbon dioxide concentrations in arteries, result in poor oxygen delivery to the brain. We stipulate that PVL occurs as a consequence.

This paper is concerned with predicting the occurrence of periventricular leukomalacia (PVL) using vital and blood gas data which are collected over a period of 12 h after the neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: 1) high resolution (vital), and 2) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DTs) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude 20 min variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first 6 h of data contain sufficient information for PVL occurrence prediction.

Collagen scaffolds have been widely employed as a dermal equivalent to induce fibroblast infiltrations and dermal regeneration in the treatment of chronic wounds and diabetic foot ulcers. Cross-linking methods have been developed to address the disadvantages of the rapid degradation associated with collagen-based scaffolds. To eliminate the potential drawbacks associated with glutaraldehyde cross-linking, methods using a water soluble carbodiimide have been developed. In the present study, the glycosaminoglycan (GAG) hyaluronic acid (HA), was covalently attached to an equine tendon derived collagen scaffold using 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide (EDC) to create ntSPONGE The HA was shown to be homogeneously distributed throughout the collagen matrix. In vitro analyses of the scaffold indicated that the cross-linking enhanced the biological stability by decreasing the enzymatic degradation and increasing the thermal denaturation temperature. The material was shown to support the attachment and proliferation of mouse L929 fibroblast cells. In addition, the cross-linking decreased the resorption rate of the collagen as measured in an intramuscular implant model in rabbits. The material was also shown to be biocompatible in a variety of in vitro and in vivo assays. These results indicate that this cross-linked collagen-HA scaffold, ntSPONGE has the potential for use in chronic wound healing.

This paper is concerned with predicting the occurrence of Periventricular Leukomalacia (PVL) using vital data which are collected over a period of twelve hours after neonatal cardiac surgery. The vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), and oxygen saturation (SpO2). Various features are extracted from the data and are then ranked so that an optimal subset of features that have the highest discriminative capabilities can be selected. A decision tree (DT) is then developed for the vital data in order to identify the most important vital measurements. The DT result shows that high amplitude 20 minutes variations and low sample entropy in the data is an important factor for prediction of PVL. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of the collected data, we show that the first six hours of data contain sufficient information for PVL occurrence prediction.

This paper is concerned with the optimization of the cardiopulmonary resuscitation (CPR) procedure, which plays a critical rule in saving the life of patients suffering from cardiac arrest. In this paper, we define the performance index for optimization using the oxygen delivery. A model developed earlier is used to calculate the oxygen delivery through CPR. The free parameters of this model which depend on the rescuer performance are ventilation time, compression speed, tidal volume, and fraction of oxygen in the inspired air. Two different optimization problems are carried out. First, a global optimization is implemented to discover the best values of the free parameters which maximize the oxygen delivery. In addition to this, a sequential optimization scheme is explored which uses a two step optimization in each CPR sequence to maximize the oxygen delivery. Results show that the sequential optimization procedure will enhance the performance of the CPR significantly.

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected at Children's Hospital of Philadelphia (CHOP) are used for this study. Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and minimum and maximum values of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in the blood gas data, maximum rate of change of concentration of bicarbonate ions in blood (HCO(3)) and minimum rate of change of partial pressure of dissolved CO(2) in the blood (PaCO(2)) are the two most important factors for prediction of the PVL. Also important are the kurtosis of heart rate and hemoglobin values.

We consider the problems of chaos and parametric control in nonlinear systems under an asymmetric potential subjected to a multiscale type excitation. The lower bound line for horseshoes chaos is analyzed using the Melnikov's criterion for a transition to permanent or transient nonperiodic motions, complement by the fractal or regular shape of the basin of attraction. Numerical simulations based on the basins of attraction, bifurcation diagrams, Poincaré sections, Lyapunov exponents, and phase portraits are used to show how stationary dissipative chaos occurs in the system. Our attention is focussed on the effects of the asymmetric potential term and the driven frequency. It is shown that the threshold amplitude ∣γ(c)∣ of the excitation decreases for small values of the driven frequency ω and increases for large values of ω. This threshold value decreases with the asymmetric parameter α and becomes constant for sufficiently large values of α. γ(c) has its maximum value for asymmetric load in comparison with the symmetric load. Finally, we apply the Melnikov theorem to the controlled system to explore the gain control parameter dependencies.

Objective: In this paper a new nonlinear system identification approach is developed for dynamical quantification of cardiovascular regulation. This approach is specifically focused on the identification of the heart rate (HR) baroreflex mechanism. The principal objective of this paper is to improve the model accuracy in the estimation of HR by proposing a modified nonlinear model., Methods and Material: The proposed HR baroreflex model is based on inherent features of the autonomic nervous system for which we develop an adaptive neuro-fuzzy inference system (ANFIS) structure. This method allows incorporation of physiological understandings about the sympathetic and parasympathetic nerves through the selection of appropriate membership functions in the ANFIS structure. The required data for system modeling are collected from the publicly available PhysioNet database., Results: The results agree with the natural characteristics and physiological understanding of the cardiovascular regulatory system, such as delay in the parasympathetic function, durability in the function of sympathetic nerves and the correlation between the HR and the ABP signals. They also show significant improvements in HR prediction in terms of the normalized root mean square error (NRMSE) in comparison with other reported methods. We achieved to 0.191 in mean NRMSE in prediction of HR in this paper which is about 20% better than the best reported result in other researches., Conclusion: We have shown that for cardiovascular system regulation, our proposed nonlinear model is more accurate than other recently developed methods. Accurate HR baroreflex modeling enables clinicians to have more reliable information for their patients., (Copyright © 2011 Elsevier B.V. All rights reserved.)

This paper is concerned with computational modeling of a severe congenital defect called Hypoplastic left heart syndrome (HLHS) that is the most common cardiac malformation with the highest likelihood of deaths in newborns. A lumped parameter model of the HLHS circulation has been developed to study the hemodynamic variables in the various sections of the cardio-pulmonary circulation system. We applied a short-term, cycle-averaging operation to the differential equations of the HLHS model to obtain the cycle-averaged model. Study has been carried out to analyze the variation of blood flow rate in different parts due to parameter changes. Results show that the developed model, could bring a good insight into understanding of the HLHS disease.

Objective: The objective of Part II is to analyze the dataset of extracted hemodynamic features (Case 3 of Part I) through computational intelligence (CI) techniques for identification of potential prognostic factors for periventricular leukomalacia (PVL) occurrence in neonates with congenital heart disease., Methods: The extracted features (Case 3 dataset of Part I) were used as inputs to CI based classifiers, namely, multi-layer perceptron (MLP) and probabilistic neural network (PNN) in combination with genetic algorithms (GA) for selection of the most suitable features predicting the occurrence of PVL. The selected features were next used as inputs to a decision tree (DT) algorithm for generating easily interpretable rules of PVL prediction., Results: Prediction performance for two CI based classifiers, MLP and PNN coupled with GA are presented for different number of selected features. The best prediction performances were achieved with 6 and 7 selected features. The prediction success was 100% in training and the best ranges of sensitivity (SN), specificity (SP) and accuracy (AC) in test were 60-73%, 74-84% and 71-74%, respectively. The identified features when used with the DT algorithm gave best SN, SP and AC in the ranges of 87-90% in training and 80-87%, 74-79% and 79-82% in test. Among the variables selected in CI, systolic and diastolic blood pressures, and pCO(2) figured prominently similar to Part I. Decision tree based rules for prediction of PVL occurrence were obtained using the CI selected features., Conclusions: The proposed approach combines the generalization capability of CI based feature selection approach and generation of easily interpretable classification rules of the decision tree. The combination of CI techniques with DT gave substantially better test prediction performance than using CI and DT separately.

Objective: Periventricular leukomalacia (PVL) is part of a spectrum of cerebral white matter injury which is associated with adverse neurodevelopmental outcome in preterm infants. While PVL is common in neonates with cardiac disease, both before and after surgery, it is less common in older infants with cardiac disease. Pre-, intra-, and postoperative risk factors for the occurrence of PVL are poorly understood. The main objective of the present work is to identify potential hemodynamic risk factors for PVL occurrence in neonates with complex heart disease using logistic regression analysis and decision tree algorithms., Methods: The postoperative hemodynamic and arterial blood gas data (monitoring variables) collected in the cardiac intensive care unit of Children's Hospital of Philadelphia were used for predicting the occurrence of PVL. Three categories of datasets for 103 infants and neonates were used-(1) original data without any preprocessing, (2) partial data keeping the admission, the maximum and the minimum values of the monitoring variables, and (3) extracted dataset of statistical features. The datasets were used as inputs for forward stepwise logistic regression to select the most significant variables as predictors. The selected features were then used as inputs to the decision tree induction algorithm for generating easily interpretable rules for prediction of PVL., Results: Three sets of data were analyzed in SPSS for identifying statistically significant predictors (p<0.05) of PVL through stepwise logistic regression and their correlations. The classification success of the Case 3 dataset of extracted statistical features was best with sensitivity (SN), specificity (SP) and accuracy (AC) of 87, 88 and 87%, respectively. The identified features, when used with decision tree algorithms, gave SN, SP and AC of 90, 97 and 94% in training and 73, 58 and 65% in test. The identified variables in Case 3 dataset mainly included blood pressure, both systolic and diastolic, partial pressures pO(2) and pCO(2), and their statistical features like average, variance, skewness (a measure of asymmetry) and kurtosis (a measure of abrupt changes). Rules for prediction of PVL were generated automatically through the decision tree algorithms., Conclusions: The proposed approach combines the advantages of statistical approach (regression analysis) and data mining techniques (decision tree) for generation of easily interpretable rules for PVL prediction. The present work extends an earlier research [Galli KK, Zimmerman RA, Jarvik GP, Wernovsky G, Kuijpers M, Clancy RR, et al. Periventricular leukomalacia is common after cardiac surgery. J Thorac Cardiovasc Surg 2004;127:692-704] in the form of expanding the feature set, identifying additional prognostic factors (namely pCO(2)) emphasizing the temporal variations in addition to upper or lower values, and generating decision rules. The Case 3 dataset was further investigated in Part II for feature selection through computational intelligence.

The osteoinductivity of demineralized bone matrix (DBM) varies from donor to donor as a result of varying levels of multiple growth factors, matrix integrity, and artifacts from material processing. Many in vitro assays are currently used for screening the osteoinductivity of DBM. The objectives of this study were to determine the correlation of specific growth factors and in vitro mitotic stimulation to in vivo ectopic bone formation capacity with a large number of DBM samples. Samples were assayed using ELISA methods for BMP-2/4 and TGF-beta1 (n = 304) and cell proliferation using SAOS-2 osteoblasts (n = 239). All samples were then implanted intramuscularly in the abdomen of nude rats. All in vitro assays showed significant variability for any particular level of ostoinductivity determined by in vivo model. A significant, but only very weak, positive correlation to in vivo results was found for TGF-beta1 (r(2) = 0.016), BMP 2/4 (r(2) = 0.065), and SAOS-2 cell proliferation (r(2) = 0.053). The results of this study amplify the notion that a multitude of factors and their relative interplay, rather than a single factor are likely to determine the potency of any particular lot of DBM.

Background: Hyperbaric oxygen (HBO) inhibits ischemia reperfusion (IR) -induced neutrophil adhesion to endothelium through an unknown mechanism. This study evaluates the effect of HBO on IR-stimulated neutrophil adhesion and polarization of expressed CD18 adhesion molecules using a novel in vitro adhesion assay and confocal microscopy., Materials and Methods: Neutrophils from normal animals were isolated from whole blood and incubated with plasma from rat gracilis muscle flaps on coverslips pretreated with ICAM. Percent adherence to ICAM and CD18 polarization was evaluated in the following five groups: (1) Nonischemic control, n = 15; (2) 4 h ischemia (IR, n = 15); (3) 4 h ischemia with HBO treatment (100% oxygen at 2.5 atmospheres absolute (IR + HBO, n = 15)); (4) 4 h ischemia with 100% oxygen at room temperature and pressure (RTP) (IR + normobaric hyperoxia, n = 5); and (5) 4 h ischemia with 8% oxygen at 2.5 atmospheres absolute (IR + hyperbaric normoxia, n = 5). Direct HBO treatment of neutrophils was also evaluated., Results: Neutrophils exposed to IR plasma showed a significant increase in percent adherent (0.8 +/- 0.1% versus 16.7 +/- 2.2%, P < 0.05) and polarized cells (6.2 +/- 1.7% versus 43.9 +/- 12.2%, P < 0.05) compared to controls. Hyperbaric oxygen significantly reduced the adhesion and polarization to 1.6 +/- 0.3 and 4.1 +/- 2.5%, respectively (P = < 0.05). Normobaric hyperoxia and hyperbaric normoxia did not affect neutrophil adherence or CD18 polarization following IR. Direct HBO treatment of neutrophils did not change the percent of polarized cells in IR., Conclusions: Hyperbaric oxygen inhibits IR-induced neutrophil adhesion by blocking CD18 surface polarization and requires plasma exposure to HBO. Treatment with oxygen or pressure alone is not effective.

Biologic extracellular matrix (ECM)-based wound dressings offer promise in the treatment of chronic wounds. A variety of animal and human tissue derived dressings are currently available. These products are processed and/or sterilized by a range of chemical and mechanical processes, which will impact the final quality of the matrices--ultimately affecting the overall wound healing process. A novel method is described to prepare a sterile and biocompatible biological wound dressing. The advantages and the clinical implications of this wound dressing preparation are also discussed.

Ischemia-reperfusion-induced neutrophil adhesion to endothelium is CD18-dependent, but information regarding polarity of CD18 adhesion molecules remains speculative. This study evaluated neutrophil adhesion using an in vitro cell adhesion assay and introduces a quantitative method of measuring CD18 membrane distribution using confocal microscopy. Neutrophils from normal animals were isolated from whole blood and incubated with plasma from rat gracilis muscle flaps with no ischemia and reperfusion (nonischemic control, n = 10) or 4 hours of ischemia and 90 minutes of reperfusion (ischemia/reperfusion, n = 10), on coverslips pretreated with and without (phosphate-buffered saline) soluble intercellular adhesion molecules. Coverslips without intercellular adhesion molecules represented a negative control (intercellular adhesion molecules were required for adhesion). Percent adherence to intercellular adhesion molecules was expressed as a ratio of adherent cells/total cells. CD18 polarization was assessed by staining neutrophils with fluorescein isothiocyanate-labeled anti-CD11b, followed by confocal microscopy and Z-stack analysis. Membrane-associated CD18 was expressed as fluorescence intensity units in three equal areas of the cell membrane. Capping was defined as twice as much fluorescence in 33 percent of the cell membrane as in the remaining 67 percent. Neutrophils exposed to ischemia and reperfusion plasma showed a significant increase in adhesion (0.8 +/- 0.1 percent versus 16.7 +/- 2.2 percent, p < 0.001) and CD18 polarization (6.2 +/- 1.7 percent versus 43.9 +/- 12.2 percent, p = 0.0206) compared with controls. This article describes an in vitro assay that reliably reproduces the neutrophil adhesion phenomenon associated with ischemia-reperfusion injury. Results from confocal microscopy allowed for quantitative estimation of membrane-associated receptor polarization.

Metabolism of arachidonic acid by the cyclo-oxygenase (COX) pathway generates a family of prostanoid mediators. Nonsteroidal anti-inflammatory drugs (NSAIDs) act by inhibiting COX, thereby reducing prostanoid synthesis. The efficacy of these agents in reducing inflammation suggests a dominant proinflammatory role for the COX pathway. However, the actions of COX metabolites are complex, and certain prostanoids, such as PGE(2), in some circumstances actually inhibit immune and inflammatory responses. In these studies, we examine the hypothesis that anti-inflammatory actions of NSAIDs may be due, in part, to inhibition of thromboxane A(2) synthesis. To study the immunoregulatory actions of thromboxane A(2), we used mice with a targeted disruption of the gene encoding the thromboxane-prostanoid (TP) receptor. Both mitogen-induced responses and cellular responses to alloantigen were substantially reduced in TP(-/-) spleen cells. Similar attenuation was observed with pharmacological inhibition of TP signaling in wild-type splenocytes, suggesting that reduced responsiveness was not due to subtle developmental abnormalities in the TP-deficient mice. The absence of TP receptors reduced immune-mediated tissue injury following cardiac transplant rejection, an in vivo model of intense inflammation. Taken together, these findings show that thromboxane augments cellular immune responses and inflammatory tissue injury. Specific inhibition of the TP receptor may provide a more precise approach to limit inflammation without some of the untoward effects associated with NSAIDs.

In vitro, chemokines can both activate and induce migration of NK cells. However, little is known about how chemokines influence NK cell activity in vivo. We studied the role of CX(3)CL1 and its receptor, CX(3)CR1, in modulating NK cell activity in an established in vivo model of tumour cell clearance. Radiolabelled YAC-1 target cells intravenously injected into C57BL/6 mice rapidly localize to the lungs and are cleared by NK cells. In mice pre-treated with blocking anti-CX(3)CL1 or anti-CX(3)CR1 Ab, target cell clearance decreased by four- to fivefold (p<0.001). In vitro, we found no effect of anti-CX(3)CL1 or anti-CX(3)CR1 Ab on NK lysis of target cells. We further examined adhesion of NK cells to Py-4-1 endothelial cells. NK cell binding to activated endothelial monolayers was significantly inhibited by anti-CX(3)CR1 Ab or soluble CX(3)CL1 (p<0.001). These studies identify a critical role for CX(3)CL1 in modulating NK cell activity in vivo.

Coordinated rearrangements of the actin-myosin cytoskeleton facilitate early and late events in T cell activation and signal transduction. As many important features of cell shape rearrangement involve small GTP-binding proteins, we examined the contribution of Rho kinase to the functions of mature T cells. Inhibitors of the Rho kinase pathway all had similar actions to inhibit the proliferation of primary lymphocyte cultures. Likewise, transfection of the human Jurkat T cell line with a dominant negative, kinase-defective mutant of Rho kinase diminished Jurkat cell proliferation. Furthermore, inhibition of Rho kinase substantially attenuated the program of cytokine gene expression that characterizes T cell activation, blocked actomyosin polymerization, and prevented aggregation of the TCR/CD3 complex colocalized with lipid rafts. These actions are relevant to immune responses in vivo, as treatment with a Rho kinase inhibitor considerably prolonged the survival of fully allogeneic heart transplants in mice and diminished intragraft expression of cytokine mRNAs. Thus, Rho GTPases acting through Rho kinase play a unique role in T cell activation during cellular immune responses by promoting structural rearrangements that are critical for T cell signaling.

Production of prostaglandin E(2) (PGE(2)) is enhanced during inflammation, and this lipid mediator can dramatically modulate immune responses. There are four receptors for PGE(2) (EP1-EP4) with unique patterns of expression and different coupling to intracellular signaling pathways. To identify the EP receptors that regulate cellular immune responses, we used mouse lines in which the genes encoding each of the four EP receptors were disrupted by gene targeting. Using the mixed lymphocyte response (MLR) as a model cellular immune response, we confirmed that PGE(2) has potent antiproliferative effects on wild-type responder cells. The absence of either the EP1 or EP3 receptors did not alter the inhibitory response to PGE(2) in the MLR. In contrast, when responder cells lacked the EP2 receptor, PGE(2) had little effect on proliferation. Modest resistance to PGE(2) was also observed in EP4-/- responder cells. Reconstitution experiments suggest that EP2 receptors primarily inhibit the MLR through direct actions on T cells. Furthermore, PGE(2) modulates macrophage function by activating the EP4 receptor and thereby inhibiting cytokine release. Thus, PGE(2) regulates cellular immune responses through distinct EP receptors on different immune cell populations: EP2 receptors directly inhibit T cell proliferation while EP2 and EP4 receptors regulate antigen presenting cells functions.

The hallmark of acute allograft rejection is infiltration of the inflamed graft by circulating leukocytes. We studied the role of fractalkine (FKN) and its receptor, CX(3)CR1, in allograft rejection. FKN expression was negligible in nonrejecting cardiac isografts but was significantly enhanced in rejecting allografts. At early time points, FKN expression was particularly prominent on vascular tissues and endothelium. As rejection progressed, FKN expression was further increased, with prominent anti-FKN staining seen around vessels and on cardiac myocytes. To determine the capacity of FKN on endothelial cells to promote leukocyte adhesion, we performed adhesion assays with PBMC and monolayers of TNF-alpha-activated murine endothelial cells under low-shear conditions. Treatment with either anti-FKN or anti-CX(3)CR1-blocking Ab significantly inhibited PBMC binding, indicating that a large proportion of leukocyte binding to murine endothelium occurs via the FKN and CX(3)CR1 adhesion receptors. To determine the functional significance of FKN in rejection, we treated cardiac allograft recipients with daily injections of anti-CX(3)CR1 Ab. Treatment with the anti-CX(3)CR1 Ab significantly prolonged allograft survival from 7 +/- 1 to 49 +/- 30 days (p < 0.0008). These studies identify a critical role for FKN in the pathogenesis of acute rejection and suggest that FKN may be a useful therapeutic target in rejection.

DNA is a complex macromolecule the immunological properties of which depend on short sequence motifs called CpG motifs or immunostimulatory sequences (ISS). These sequences are mitogenic for B cells and can stimulate macrophage cytokine production. While these sequences do not directly activate T cells, they can augment effects of stimulation via the TCR. Furthermore, ISS can affect T cells because of macrophage production of IL-12 and IFN-alpha/beta. In these studies, we further evaluated the immune effects of DNA on T cells, testing the possibility that certain T cell populations can respond directly to this stimulus. We therefore tested the in vitro responses of thymocytes to a series of phosphodiester (Po) and phosphorothioate (Ps) oligonucleotides (ODNs) varying in sequence. In in vitro cultures, phosphorothioate ODNs (sODNs) containing CpG motifs induced significant proliferation of murine thymocytes, although phosphodiester compounds lacked activity. The magnitude of stimulation varied with sequences flanking the CpG motifs, as both dA and dT sequences enhanced the stimulatory capacity of the CpG motif. Furthermore, CpG sODNs were strong costimulators of anti-CD3-mediated thymocyte activation, increasing proliferation compared to anti-CD3 in the absence of DNA. This activation was only partially inhibited by cyclosporine A and was not dependent on a calcium influx. Together, these results indicate that phosphorothioate oligonucleotides containing CpG motifs can directly induce thymocyte proliferation as well as augment TCR activation. These observations thus extend the range of actions of CpG DNA and suggest additional mechanisms for its function as an immunomodulatory agent or adjuvant., (Copyright 2000 Academic Press.)

Mice infected with Listeria monocytogenes (LM) generate CD8 effectors specific for f-MIGWII, the amino terminus of the bacterial product lemA presented by the class Ib MHC molecule H2 M3wt. lemA has several distinctive properties: 1) it is readily presented as an exogenous Ag in the absence of bacterial infection; 2) it is processed by a TAP-independent pathway, which is sensitive to chloroquine, pepstatin, and brefeldin; and 3) the immunogenic portion of the molecule is extremely resistant to proteolytic degradation even by proteinase K. To assess the structural basis for these findings, we expressed a truncated variant (t-lemA) containing the amino-terminal hexapeptide and the subsequent 27 amino acids linked to a histidine tail in Escherichia coli, and purified the product by affinity chromatography. Purified t-lemA could be presented to f-MIGWII-specific effectors by macrophages and fibroblasts at 1-10 nM. Unlike f-MIGWII, which binds directly to H2 M3wt, t-lemA required processing by a chloroquine-, pepstatin-, and brefeldin-sensitive pathway. Brefeldin sensitivity often implies endogenous processing in the cytoplasm, but several lines of evidence suggest translocation to the cytoplasm and proteosomal degradation are not critical for t-lemA presentation. Unlike f-MIGWII, t-lemA was profoundly resistant to proteinase K, and, using 35S-labeled t-lemA, we could identify the region from position 1 to approximately 30 as the protease-resistant element. Thus, the hydrophobic peptide sequence following f-MIGWII can account for the unusual properties of lemA noted above. Analogous modification could be used to alter the properties of other peptide Ags presented by class I MHC products.

The renin-angiotensin system (RAS) is a key regulator of vascular tone and blood pressure. In addition, angiotensin II also has a number of cellular effects that may contribute to disease pathogenesis. Using Agtr1a(-/-) mice, which lack AT(1A) receptors for angiotensin II, we have identified a novel function of the RAS to modulate the immune system. We find that angiotensin II, acting through type 1 (AT(1)) receptors on immune cells, triggers the proliferation of splenic lymphocytes. These actions contribute to the vigor of cellular alloimmune responses. Within lymphoid organs, sufficient components of the RAS are present to activate AT(1) receptors during an immune response, promoting cell growth. These actions require activation of calcineurin phosphatase. In an in vivo model of cardiac transplantation, the absence of AT(1) signaling accentuates the immunosuppressive effects of the calcineurin inhibitor cyclosporine. We conclude that inhibition of AT(1) receptor signaling should be useful as an anti-inflammatory and immunosuppressive therapy. Furthermore, the actions of the RAS to promote lymphocyte activation may contribute to inflammation that characterizes a number of diseases of the heart and the vascular system.

Background: Carbon dioxide (CO(2)) pneumoperitoneum has been implicated as a possible factor in depressed intraperitoneal immunity. Using in vitro functional assays, CO(2) has been shown to decrease the function of peritoneal macrophages harvested from insufflated mice. However, an effective in vivo assessment is lacking. Listeria monocytogenes (LM), an intracellular pathogen, has served as a well-established in vivo model to study cell-mediated immune responses in mice. This study examines the immune competence of mice based on their ability to clear intraperitoneally administered LM following CO(2) vs helium (He) insufflation., Methods: Eighty-five mice (C57Bl/6, males, 4-6 weeks old) were divided between the following four treatment groups: CO(2) insufflation, He insufflation, abdominal laparotomy (Lap), and control (anesthesia only). Immediately postoperatively, each group was inoculated percutaneously and intraperitoneally with a sublethal dose (.015 x 10(6) org) of virulent LM (EGD strain). Half of the animals were killed on postoperative day 3 and half on day 5. Spleens and livers (sites of bacterial predilection) were harvested, homogenized, and plated on TSB agar. The amount of bacteria (1 x 10(6) LM/spleen and liver) from each group was then compared. Statistical significance was set at p

Allospecific CD8(+) T lymphocytes are an important component of the cellular response in allograft rejection. These cells recognize and engage MHC class I antigens, leading to allospecific cytolytic responses and graft rejection. In mouse kidney allografts that survive to 3 wk after transplantation, we noted that the majority of CD8(+) cells do not express surface alpha/beta T cell receptor alpha/beta(TCR), gamma/deltaTCR, or CD3. However, these CD8(+)TCR- cells did express surface markers characteristic of T cells, including Thy1.2, CD2, and CD5. In addition, the CD8(+)TCR- cells expressed mRNA for TCR Vbeta gene families, and nearly half stained positive for cytoplasmic Vbeta8 protein, suggesting that they are T cells that have downregulated alpha/betaTCR protein expression from their cell surfaces. When these surface TCR- cells were isolated from kidney allografts by flow cytometry and cultured in the presence of either allogeneic or syngeneic stimulators, nearly 100% of cells reacquired normal levels of alpha/betaTCR expression with disproportionate usage of Vbeta8 chains. After recovery of their surface TCR expression, the CD8(+)TCR- population demonstrated strong alloreactivity in culture. These results suggest that the substantial number of CD8(+)TCR- cells found in long-term surviving mouse kidney allografts are alpha/beta-T cells that have downregulated their cell surface expression of TCR. While in other systems this phenotype may identify cells that have engaged antigen, our results indicate that loss of TCR expression by CD8(+) kidney graft-infiltrating cells may not depend on antigen engagement and that elements in the microenvironment of the kidney graft play a key role in this process. Factors that modulate expression of TCR by graft-infiltrating lymphocytes may have an important role in regulating rejection responses.

A subset of H2M3wt-restricted, Listeria monocytogenes (LM)-immune CD8 effectors recognize antigen-presenting cells (APC) preincubated with heat-killed LM. The responsible product, which we have previously designated heat-killed Listeria-associated antigen (HAA), is extremely hydrophobic and resistant to proteolytic degradation. Despite the protease resistance of HAA, we now report that HAA-immune clones are uniformly responsive to fMIGWII, a formylated oligopeptide derived from the recently described LM product, lemA. While fMIGWII was by far the most potent peptide tested, over half our clones also responded to the LM-derived peptide fMIVII and cross-reactive responses to two other unrelated formylated peptides at concentrations of <1 microM were frequently observed. One of these peptides (fBlaZ) did not share any amino acid in common with fMIGWII except N-formyl methionine at position 1. Unformylated variants of the same peptides were inactive. HAA-immune CD8 cells also responded in an H2M3wt-restricted manner to APC pretreated with heat-killed or live preparations of other gram-positive and gram-negative bacteria such as Streptococcus pyogenes (SP) and Proteus vulgaris (PV). Unlike fMIGWII which is water soluble and protease sensitive, the native antigens extracted from SP and PV, like HAA, were very hydrophobic and proteinase K resistant, presumably reflecting in each case the association of cross-reactive polypeptides with bacterial lipid or phospholipid. Thus, HAA/lemA-immune, H2M3wt-restricted effectors can respond to a variety of formylated peptides and bacterial antigens in vitro. Similar cross-reactions in vivo might have physiologically significant implications.

Mice infected with Listeria monocytogenes (LM) generate protective CD8 cells of varying specificity. One subset, unlike conventional LM-immune CD8 cells, can respond to antigen-presenting cells (APC) treated with heat-killed LM (HKLM). These cells proved to have surprisingly uniform specificity, recognizing a product we designated HKLM-associated antigen (HAA) presented by the non-classical class Ib product H2-M3wt. HAA proved to be extremely hydrophobic and the bioactive portion of the molecule was highly protease-resistant, leading us initially to speculate that it might be a non-peptide. Recent studies, however, identify HAA as a complex containing lemA, a listerial protein bearing the immunogenic amino terminal peptide sequence fMIGWII, tightly associated with bacterial cardiolipin. A variety of cell types can process and present exogenous HAA/lemA, and the phospholipid component appears essential for this processing. Endosomal acidification and proteolysis are required for processing, but the site where antigen binds to H2-M3wt within APC remains uncertain. HAA/lemA-immune effectors are unusually cross-reactive. We could readily detect H2-M3wt-restricted responses to APC incubated with unrelated N-formylated peptides, and bacteria. HAA-like products represent an intriguing new set of bacterial antigens recognizable by immune CD8 cells.

The mechanism of immunosuppression induced by bovine herpesvirus 1 (BHV-1) was investigated by studying the effects of the virus on the expression of major histocompatibility complex (MHC) class I molecules. After infection with the virus, the expression of class I molecules was detected by flow cytometry and pulse-chase analysis. A selective downregulation of expression of class I molecules was seen in the infected cells, while the class II expression remained unaffected. The reduction in surface expression was evident as early as 8 hours postinfection, reaching significant levels by 12 hours. The downregulation was seen with a multiplicity of infection as low as 0.1. A modified live vaccine strain of BHV-1 also induced the downregulation of class I expression. Analysis of the viral proteins(s) involved in this downregulation with metabolic inhibitors (cycloheximide or phosphonoacetic acid), suggested that the immediate early and/or early proteins of the virus mediate this effect. Pulse-chase analysis revealed that the synthesis of the class I heavy chain, and the assembly/transport of class I molecules were affected by the virus infection. These results suggest that BHV-1 interferes with the molecular mechanisms involved in the synthesis, and assembly/transport of MHC-class I molecules. This interference with the class I antigen processing pathway might help the virus to evade the cytotoxic T-lymphocyte response of the host.

To identify the role of donor class I alloantigens in regulating the CD8+ T cell response to a kidney allograft, we analyzed and compared the CD8+ infiltrate in kidney transplants from MHC class I-deficient (class I-) mouse donors and class I+ controls. One week after transplantation, there was a prominent CD8+ infiltrate in control allografts, whereas CD8+ T cells were virtually absent in grafts from class I- donors. In class I+ allografts, infiltrating CD8+ cells utilized a wide range of T cell receptor (TCR) Vbeta families and their Vbeta usage was similar to that of the systemic CD8+ population. However, there was a modest but significant overrepresentation of cells bearing Vbeta8 in the graft compared with the spleen due to an expansion of CD8+ Vbeta8.3+ cells. This could be detected as early as 1 week and became more pronounced by 3 weeks after transplantation. In 3-week allografts, only 52% of CD8+ cells expressed alphabetaTCR. Among T cells isolated from class I+ grafts, the CD8+ Vbeta8+ cells demonstrated allospecific responses that were numerically larger than responses of the CD8+ Vbeta8- population. In contrast to the early (1 week) time point, significant numbers of CD8+ cells could be isolated from class I- grafts by 3 weeks after transplantation and their Vbeta repertoire resembled that seen in controls. While increasing numbers of CD8+ Vbeta8+ were present in the class I- grafts at 3 weeks, this increase was not statistically significant. Thus, expression of class I alloantigens on a kidney graft plays an important role in regulating the rate of accumulation of CD8+ T cells in rejecting kidney grafts. However, the TCR Vbeta repertoire of the CD8+ T cell infiltrate is largely determined by factors that are independent of normal class I expression on the graft.

Mice infected with Listeria monocytogenes (LM) generate H2-M3wt-restricted CD8 effectors which recognize a heat-killed LM-associated antigen (HAA) presented by macrophages. To characterize HAA, we extracted a bioactive component from LM using SDS or NaOH. Extracted HAA aggregated in hydrophilic solvents but dissociated in the presence of SDS into a smaller subunit which migrated in Sephadex G-200 between chymotrypsinogen (25 kDa) and cytochrome c (12.5 kDa). HAA bioactivity and size was unaffected by proteinase K under conditions which degraded virtually all detectable protein. HAA was also unaffected by other proteases, RNase and DNase, but HAA bioactivity was destroyed by periodate, an agent that degrades carbohydrates. These studies demonstrate that H2-M3wt can present a hydrophobic, non-peptide, microbial antigen, probably glycolipid in origin, to CD8 T cells.

To evaluate the requirement for CD8+ T cells in kidney transplant rejection, we studied class I-deficient (class I-) mice that had received vascularized renal allografts. Because of the absence of MHC class I expression, these mice are grossly deficient in CD4-CD8+ alpha beta TCR+ cells. Despite the deficiency of CD8+ T cells in naive class I- mice, kidney allografts transplanted into class I- recipients developed significant reductions in renal blood flow and glomerular filtration rate to levels comparable to allograft controls. This functional deterioration was associated with histologic changes consistent with cellular rejection. There were no significant differences in the pattern, severity, or phenotypic character of the cellular infiltrate in allografts transplanted into class I- recipients compared to controls. In fact, substantial numbers of CD8+ T cells were present in these allografts, and the intensity and pattern of anti-CD8 staining was not different from controls. Virtually all of the CD8+ cells in the kidney grafts were class I- and CD4- and co-expressed CD8 alpha and beta chains; the majority were alpha beta TCR+. The CD8+ infiltrating cells were cytotoxic to donor targets but also exhibited activity against class I+ cells bearing self-MHC. Despite the marked CD8+ T cell infiltration of grafts, CD8+ T cells could not be detected by flow cytometry in freshly isolated splenocytes from the class I- recipients of allografts. High levels of circulating anti-class I antibodies were present in the serum of class I- recipients of kidney allografts, and these antibodies had unusual specificity in that they appeared to recognize framework epitopes of MHC class I. Thus, class I- mice readily reject kidney allografts. Although the number of CD8+ alloreactive precursors is substantially reduced in class mice, and their specificities are atypical, the pattern and character of the intra-graft CD8+ cellular response is not significantly altered. Thus, factors unrelated to precursor frequency determine the dimension of the intra-graft CD8+ response. Such factors might include cellular and/or biochemical properties of microenvironment within the graft.