Effects of Combined Bevacizumab and Paclitaxel on Tumor Interstitial Fluid Pressure in a Preclinical Breast Cancer Model

Effects of Combined Bevacizumab and Paclitaxel on Tumor Interstitial Fluid Pressure in a Preclinical Breast Cancer Model by Ricardo H. Alvarez Several mechanisms of cell resistance are often accountable for unsuccessful chemotherapy against cancer. Another reason, which has received increased attention, is the inefficient transport of anticancer drugs into tumor tissue. These impaired transports of chemotherapy into the tumor have been attributed to abnormal microvasculature and to pathologically increased tumor hypertension also called: interstitial fluid pressure (IFP). The pathophysiological processes leading to elevated tumor IFP are poorly understood. Here, in a preclinical breast cancer model, it is argued that a condition of raised IFP is a major factor in preventing optimal access of systemically administered chemotherapy agents. In our experimental model, we used a GILM2 human breast cancer in xenografts; mice were treated with different doses of paclitaxel –a widely used antimicrotubular agent, and bevacizumab –monoclonal antibody against vascular endothelial growth factor (VEGF). The proposed research project is designed to test the hypothesis that paclitaxel in combination with bevacizumab decreases the tumor IPF by restoring tumor permeability and increasing chemotherapy delivery. We demonstrated that the combination of paclitaxel and bevacizumab produced greater tumor control than either agent given alone and this combination reduced the IFP, producing an increment of 75% of apoptosis compared with the control arm. In addition, the intra-tumor paclitaxel quantification by liquid chromatography/Mass Spectrometry (LC/MS) demonstrated that lower dose of both agents showed a synergistic effect compared with high dose of treatment, where there is no significantly increase of paclitaxel into the tumor. These preclinical results are likely to have broad implications for the utility of anti-angiogenic therapies alone and in combination with chemotherapeutic agents.

EVALUATION OF INTENSITY MODULATED RADIATION THERAPY (IMRT) DELIVERY ERROR DUE TO IMRT TREATMENT PLAN COMPLEXITY AND IMPROPERLY MATCHED DOSIMETRY DATA

Intensity modulated radiation therapy (IMRT) is a technique that delivers a highly conformal dose distribution to a target volume while attempting to maximally spare the surrounding normal tissues. IMRT is a common treatment modality used for treating head and neck (H&N) cancers, and the presence of many critical structures in this region requires accurate treatment delivery. The Radiological Physics Center (RPC) acts as both a remote and on-site quality assurance agency that credentials institutions participating in clinical trials. To date, about 30% of all IMRT participants have failed the RPC’s remote audit using the IMRT H&N phantom. The purpose of this project is to evaluate possible causes of H&N IMRT delivery errors observed by the RPC, specifically IMRT treatment plan complexity and the use of improper dosimetry data from machines that were thought to be matched but in reality were not. Eight H&N IMRT plans with a range of complexity defined by total MU (1460-3466), number of segments (54-225), and modulation complexity scores (MCS) (0.181-0.609) were created in Pinnacle v.8m. These plans were delivered to the RPC’s H&N phantom on a single Varian Clinac. One of the IMRT plans (1851 MU, 88 segments, and MCS=0.469) was equivalent to the median H&N plan from 130 previous RPC H&N phantom irradiations. This average IMRT plan was also delivered on four matched Varian Clinac machines and the dose distribution calculated using a different 6MV beam model. Radiochromic film and TLD within the phantom were used to analyze the dose profiles and absolute doses, respectively. The measured and calculated were compared to evaluate the dosimetric accuracy. All deliveries met the RPC acceptance criteria of ±7% absolute dose difference and 4 mm distance-to-agreement (DTA). Additionally, gamma index analysis was performed for all deliveries using a ±7%/4mm and ±5%/3mm criteria. Increasing the treatment plan complexity by varying the MU, number of segments, or varying the MCS resulted in no clear trend toward an increase in dosimetric error determined by the absolute dose difference, DTA, or gamma index. Varying the delivery machines as well as the beam model (use of a Clinac 6EX 6MV beam model vs. Clinac 21EX 6MV model), also did not show any clear trend towards an increased dosimetric error using the same criteria indicated above.

The relationship of maternal low birthweight and infant low birthweight among African-American women at term delivery: Findings from the 1988 National Maternal and Infant Health Survey

A cohort study study design was used to study the relationship of maternal low birthweight and infant low birthweight among African American women delivering full term infants. The cohort consisted of 3,157 mother-infant pairs drawn from the 1988 National Maternal and Infant Health Survey conducted by the National Center for Health Statistics. The objectives of the study were (1) to determine if low birthweight, African American mothers delivering term infants experienced higher rates of infant low birthweight and (2) to examine the role of selected contributory variables in the relationship of maternal low birthweight and infant low birthweight. Contributory risk factors examined included maternal marital status, maternal age, maternal education, maternal height, maternal prepregnant weight, birth order, history of a prior low birthweight delivery, timing of prenatal care, number of prenatal visits, gestational length, infant gender, and behavioral factors of smoking, alcohol, and illicit drug use during pregnancy.^ Using logistic regression analysis, risk of infant low birthweight among maternal low birthweight mothers increased after controlling for less than a high school education, less than 20 years of age, prepregnant weight less than 100 lbs, history of a prior low birthweight delivery, birth order, smoking during pregnancy, and use of alcohol and illicit drugs during pregnancy, but was not statistically significant. Loss of statistical significance was attributed to a large reduction in cases available for analysis after including illicit drug use in the model.^ This study demonstrated a consistent pattern of increased rates of infant low birthweight among low birthweight mothers. The force of history remains, hence women with this trait should be carefully monitored and advised during pregnancy to decrease risk of a low birthweight infant, in order to decrease the chain of events leading to future generations of low birthweight mothers. ^

Enhanced in vivo angiogenesis within a model tissue engineered construct using biodegradable microspheres containing encapsulated VEGF

Introduction. Tissue engineering techniques offer a potential means to develop a tissue engineered construct (TEC) for the treatment of tissue and organ deficiencies. However, a lack of adequate vascularization is a limiting factor in the development of most viable engineered tissues. Vascular endothelial growth factor (VEGF) could aid in the development of a viable vascular network within TECs. The long-term goals of this research are to develop clinically relevant, appropriately vascularized TECs for use in humans. This project tested the hypothesis that the delivery of VEGF via controlled release from biodegradable microspheres would increase the vascular density and rate of angiogenesis within a model TEC. ^ Materials and methods. Biodegradable VEGF-encapsulated microspheres were manufactured using a novel method entitled the Solid Encapsulation/Single Emulsion/Solvent Extraction technique. Using a PLGA/PEG polymer blend, microspheres were manufactured and characterized in vitro. A model TEC using fibrin was designed for in vivo tissue engineering experimentation. At the appropriate timepoint, the TECs were explanted, and stained and quantified for CD31 using a novel semi-automated thresholding technique. ^ Results. In vitro results show the microspheres could be manufactured, stored, degrade, and release biologically active VEGF. The in vivo investigations revealed that skeletal muscle was the optimal implantation site as compared to dermis. In addition, the TECs containing fibrin with VEGF demonstrated significantly more angiogenesis than the controls. The TECs containing VEGF microspheres displayed a significant increase in vascular density by day 10. Furthermore, TECs containing VEGF microspheres had a significantly increased relative rate of angiogenesis from implantation day 5 to day 10. ^ Conclusions. A novel technique for producing microspheres loaded with biologically active proteins was developed. A defined concentration of microspheres can deliver a quantifiable level of VEGF with known release kinetics. A novel model TEC for in vivo tissue engineering investigations was developed. VEGF and VEGF microspheres stimulate angiogenesis within the model TEC. This investigation determined that biodegradable rhVEGF 165-encapsulated microspheres increased the vascular density and relative rate of angiogenesis within a model TEC. Future applications could include the incorporation of microvascular fragments into the model TEC and the incorporation of specific tissues, such as fat or bone. ^

Study of length of stay in the cardiovascular intensive care unit

Coronary artery bypass graft (CABG) surgery is among the most common operations performed in the United States and accounts for more resources expended in cardiovascular medicine than any other single procedure. CABG surgery patients initially recover in the Cardiovascular Intensive Care Unit (CVICU). The post-procedure CVICU length of stay (LOS) goal is two days or less. A longer ICU LOS is associated with a prolonged hospital LOS, poor health outcomes, greater use of limited resources, and increased medical costs. ^ Research has shown that experienced clinicians can predict LOS no better than chance. Current CABG surgery LOS risk models differ greatly in generalizability and ease of use in the clinical setting. A predictive model that identified modifiable pre- and intra-operative risk factors for CVICU LOS greater than two days could have major public health implications as modification of these identified factors could decrease CVICU LOS and potentially minimize morbidity and mortality, optimize use of limited health care resources, and decrease medical costs. ^ The primary aim of this study was to identify modifiable pre-and intra-operative predictors of CVICU LOS greater than two days for CABG surgery patients with cardiopulmonary bypass (CPB). A secondary aim was to build a probability equation for CVICU LOS greater than two days. Data were extracted from 416 medical records of CABG surgery patients with CPB, 50 to 80 years of age, recovered in the CVICU of a large teaching, referral hospital in southeastern Texas, during the calendar year 2004 and the first quarter of 2005. Exclusion criteria included Diagnosis Related Group (DRG) 106, CABG surgery without CPB, CABG surgery with other procedures, and operative deaths. The data were analyzed using multivariate logistic regression for an alpha=0.05, power=0.80, and correlation=0.26. ^ This study found age, history of peripheral arterial disease, and total operative time equal to and greater than four hours to be independent predictors of CVICU LOS greater than two days. The probability of CVICU LOS greater than two days can be calculated by the following equation: -2.872941 +.0323081 (age in years) + .8177223 (history of peripheral arterial disease) + .70379 (operative time). ^

Roadmap for implementing the Chronic Care Model for weight management: Healthcare Institution's Response to Obesity (HIRO)

Background. Excess weight and obesity are at epidemic proportions in the United States and place individuals at increased risk for a variety of chronic conditions. Rates of diabetes, high blood pressure, coronary artery disease, stroke, cancer, and arthritis are all influenced by the presence of obesity. Small reductions in excess weight can produce significant positive clinical outcomes. Healthcare organizations have a vital role to play in the identification and management of obesity. Currently, healthcare providers do not adequately diagnose and manage excess weight in patients. Lack of skill, time, and knowledge are commonly cited as reasons for non-adherence to recommended standards of care. The Chronic Care Model offers an approach to healthcare organizations for chronic disease management. The model consists of six elements that work together to empower both providers and patients to have more productive interactions: the community, the health system itself, self-management support, delivery system design, decision support, and clinical information systems. The model and its elements may offer a framework through which healthcare organizations can adapt to support, educate, and empower providers and patients in the management of excess weight and obesity. Successful management of excess weight will reduce morbidity and mortality of many chronic conditions. Purpose. The purpose of this review is to synthesize existing research on the effectiveness of the Chronic Care Model and its elements as they relate to weight management and behaviors associated with maintaining a healthy weight. Methods: A narrative review of the literature between November 1998 and November 2008 was conducted. The review focused on clinical trials, systematic reviews, and reports related to the chronic care model or its elements and weight management, physical activity, nutrition, or diabetes. Fifty-nine articles are included in the review. Results. This review highlights the use of the Chronic Care Model and its elements that can result in improved quality of care and clinical outcomes related to weight management, physical activity, nutrition, and diabetes. Conclusions. Healthcare organizations can use the Chronic Care Model framework to implement changes within their systems to successfully address overweight and obesity in their patient populations. Specific recommendations for operationalizing the Chronic Care Model elements for weight management are presented.^

Exploring race/ethnicity as a risk factor for mortality in newborns following congenital heart surgery at a center of excellence

The purpose of this study was to determine if race/ethnicity was a significant risk factor for hospital mortality in children following congenital heart surgery in a contemporary sample of newborns with congenital heart disease. Unlike previous studies that utilized administrative databases, this study utilized clinical data collected at the point of care to examine racial/ethnic outcome differences in the context of the patients' clinical condition and their overall perioperative experience. A retrospective cohort design was used. The study sample consisted of 316 newborns (<31 days of age) who underwent congenital heart surgery between January 2007 through December 2009. A multivariate logistic regression model was used to determine the impact of race/ethnicity, insurance status, presence of a spatial anomaly, prenatal diagnosis, postoperative sepsis, cardiac arrest, respiratory failure, unplanned reoperation, and total length of stay in the intensive care unit on outcomes following congenital heart surgery in newborns. The study findings showed that the strongest predictors of hospital mortality following congenital heart surgery in this cohort were postoperative cardiac arrest, postoperative respiratory failure, having a spatial anomaly, and total ICU LOS. Race/ethnicity and insurance status were not significant risk factors. The institution where this study was conducted is designated as a center of excellence for congenital heart disease. These centers have state-of-the-art facilities, extensive experience in caring for children with congenital heart disease, and superior outcomes. This study suggests that optimal care delivery for newborns requiring congenital heart surgery at a center of excellence portends exceptional outcomes and this benefit is conferred upon the entire patient population despite the race/ethnicity of the patients. From a public health and health services view, this study also contributes to the overall body of knowledge on racial/ethnic disparities in children with congenital heart defects and puts forward the possibility of a relationship between quality of care and racial/ethnic disparities. Further study is required to examine the impact of race/ethnicity on the long-term outcomes of these children as they encounter the disparate components of the health care delivery system. There is also opportunity to study the role of race/ethnicity on the hospital morbidity in these patients considering current expectations for hospital survival are very high, and much of the current focus for quality improvement rests in minimizing the development of patient morbidities.^

Policy analysis of Texas Senate Bill 532 and the regulation of nurse practitioners in the convenience care model

Background. Retail clinics, also called convenience care clinics, have become a rapidly growing trend since their initial development in 2000. These clinics are coupled within a larger retail operation and are generally located in "big-box" discount stores such as Wal-mart or Target, grocery stores such as Publix or H-E-B, or in retail pharmacies such as CVS or Walgreen's (Deloitte Center for Health Solutions, 2008). Care is typically provided by nurse practitioners. Research indicates that this new health care delivery system reduces cost, raises quality, and provides a means of access to the uninsured population (e.g., Deloitte Center for Health Solutions, 2008; Convenient Care Association, 2008a, 2008b, 2008c; Hansen-Turton, Miller, Nash, Ryan, Counts, 2007; Salinsky, 2009; Scott, 2006; Ahmed & Fincham, 2010). Some healthcare analysts even suggest that retail clinics offer a feasible solution to the shortage of primary care physicians facing the nation (AHRQ Health Care Innovations Exchange, 2010). ^ The development and performance of retail clinics is heavily dependent upon individual state policies regulating NPs. Texas currently has one of the most highly regulated practice environments for NPs (Stout & Elton, 2007; Hammonds, 2008). In September 2009, Texas passed Senate Bill 532 addressing the scope of practice of nurse practitioners in the convenience care model. In comparison to other states, this law still heavily regulates nurse practitioners. However, little research has been conducted to evaluate the impact of state laws regulating nurse practitioners on the development and performance of retail clinics. ^ Objectives. (1). To describe the potential impact that SB 532 has on retail clinic performance. (2). To discuss the effectiveness, efficiency, and equity of the convenience care model. (3). To describe possible alternatives to Texas' nurse practitioner scope of practice guidelines as delineated in Texas Senate Bill 532. (4). To describe the type of nurse practitioner state regulation (i.e. independent, light, moderate, or heavy) that best promotes the convenience care model. ^ Methods. State regulations governing nurse practitioners can be characterized as independent, light, moderate, and heavy. Four state NP regulatory types and retail clinic performance were compared and contrasted to that of Texas regulations using Dunn and Aday's theoretical models for conducting policy analysis and evaluating healthcare systems. Criteria for measurement included effectiveness, efficiency, and equity. Comparison states were Arizona (Independent), Minnesota (Light), Massachusetts (Moderate), and Florida (Heavy). ^ Results. A comparative states analysis of Texas SB 532 and alternative NP scope of practice guidelines among the four states: Arizona, Florida, Massachusetts, and Minnesota, indicated that SB 532 has minimal potential to affect the shortage of primary care providers in the state. Although SB 532 may increase the number of NPs a physician may supervise, NPs are still heavily restricted in their scope of practice and limited in their ability to act as primary care providers. Arizona's example of independent NP practice provided the best alternative to affect the shortage of PCPs in Texas as evidenced by a lower uninsured rate and less ED visits per 1,000 population. A survey of comparison states suggests that retail clinics thrive in states that more heavily restrict NP scope of practice as opposed to those that are more permissive, with the exception of Arizona. An analysis of effectiveness, efficiency, and equity of the convenience care model indicates that retail clinics perform well in the areas of effectiveness and efficiency; but, fall short in the area of equity. ^ Conclusion. Texas Senate 532 represents an incremental step towards addressing the problem of a shortage of PCPs in the state. A comparative policy analysis of the other four states with varying degrees of NP scope of practice indicate that a more aggressive policy allowing for independent NP practice will be needed to achieve positive changes in health outcomes. Retail clinics pose a temporary solution to the shortage of PCPs and will need to expand their locations to poorer regions and incorporate some chronic care to obtain measurable health outcomes. ^

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

ALLERGEN SENSITIZATION AND IMMUNOMODULATION BY SYNTHETIC LIGANDS, PUL-042, IN AN ALLERGEN-INDUCED ASTHMA MURINE MODEL

Allergen-induced asthma is the leading form of asthma and a chronic condition worldwide. Common allergens are known to contribute to the pathogenesis of this disease. Murine models of allergic asthma have mostly used an intraperitoneal route of sensitization (not airway) to study this disease. Allergic asthma pathophysiology involves the activation of TH2-specific cells, which triggers production of IgE antibodies, the up-regulation of TH2-specific cytokines (i.e. IL-4, IL-5, IL-9 and IL-13), increased airway eosinophilia, and mucin hypersecretion. Although there are several therapeutics currently treating asthmatic patients, some of these treatments can result in drug tolerance and may be linked to increased mortality. CpG oligodeoxynucleotides (ODNs) is a synthetic ligand that targets Toll-like Receptor (TLR) 9. It has been evaluated as a therapeutic agent for the treatment of cancer, infectious diseases, and for treating allergy and asthma. PUL-042 is also a synthetic TLR ligand and is composed of two agonists against TLR2/6 heterodimer and TLR9. Previous studies have evaluated PUL-042 for its ability to confer resistance against bacterial and viral lung infection. These findings, combined with studies performed using CpG ODNs, led to speculation that PUL-042 dampens the immune response in allergen-induced asthma. My thesis research investigated airway route sensitization and airway delivery of PUL-042 to evaluate its effects in reducing an allergen-induced asthma phenotype in a murine model. The results of this study contribute to the foundation for future investigations to evaluate the efficacy of PUL-042 as a novel therapy in allergic-asthma disease.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Use of Echogenic Immunoliposomes for Delivery of both Drug and Stem Cells for Inhibition of Atheroma Progression

Use of Echogenic Immunoliposomes for Delivery of both Drug and Stem Cells for Inhibition of Atheroma Progression By Ali K. Naji B.S. Advisor: Dr. Melvin E. Klegerman PhD Background and significance: Echogenic liposomes can be used as drug and cell delivery vehicles that reduce atheroma progression. Vascular endothelial growth factor (VEGF) is a signal protein that induces vasculogenesis and angiogenesis. VEGF functionally induces migration and proliferation of endothelial cells and increases intracellular vascular permeability. VEGF activates angiogenic transduction factors through VEGF tyrosine kinase domains in high-affinity receptors of endothelial cells. Bevacizumab is a humanized monoclonal antibody specific for VEGF-A which was developed as an anti-tumor agent. Often, anti-VEGF agents result in regression of existing microvessels, inhibiting tumor growth and possibly causing tumor shrinkage with time. During atheroma progression neovasculation in the arterial adventitia is mediated by VEGF. Therefore, bevacizumab may be effective in inhibiting atheroma progression. Stem cells show an ability to inhibit atheroma progression. We have previously demonstrated that monocyte derived CD-34+ stem cells that can be delivered to atheroma by bifunctional-ELIP ( BF-ELIP) targeted to Intercellular Adhesion Molecule-1 (ICAM-1) and CD-34. Adhesion molecules such as ICAM-1 and vascular cell adhesion molecule-1 (VCAM-1) are expressed by endothelial cells under inflammatory conditions. Ultrasound enhanced liposomal targeting provides a method for stem cell delivery into atheroma and encapsulated drug release. This project is designed to examine the ability of echogenic liposomes to deliver bevacizumab and stem cells to inhibit atheroma progression and neovasculation with and without ultrasound in vitro and optimize the ultrasound parameters for delivery of bevacizumab and stem cells to atheroma. V Hypotheses: Previous studies showed that endothelial cell VEGF expression may relate to atherosclerosis progression and atheroma formation in the cardiovascular system. Bevacizumab-loaded ELIP will inhibit endothelial cell VEGF expression in vitro. Bevacizumab activity can be enhanced by pulsed Doppler ultrasound treatment of BEV-ELIP. I will also test the hypothesis that the transwell culture system can serve as an in vitro model for study of US-enhanced targeted delivery of stem cells to atheroma. Monocyte preparations will serve as a source of CD34+ stem cells. Specific Aims: Induce VEGF expression using PKA and PKC activation factors to endothelial cell cultures and use western blot and ELISA techniques to detect the expressed VEGF.  Characterize the relationship between endothelial cell proliferation and VEGF expression to develop a specific EC culture based system to demonstrate BEV-ELIP activity as an anti-VEGF agent. Design a cell-based assay for in vitro assessment of ultrasound-enhanced bevacizumab release from echogenic liposomes.  Demonstrate ultrasound delivery enhancement of stem cells by applying different types of liposomes on transwell EC culture using fluorescently labeled monocytes and detect the effect on migration and attachment rate of these echogenic liposomes with and without ultrasound in vitro.