The contamination of the environment, accidental or intentional, in particular with chemical toxins such as industrial chemicals and chemical warfare agents has increased public fear. There is a critical requirement for the continuous detection of toxins present at very low levels in the environment. Indeed, some ultra-sensitive analytical techniques already exist, for example chromatography and mass spectroscopy, which are approved by the US Environmental Protection Agency for the detection of toxins. However, these techniques are limited to the detection of known toxins. Cellular expression of genomic and proteomic biomarkers in response to toxins allows monitoring of known as well as unknown toxins using Polymerase Chain Reaction and Enzyme Linked Immunosensor Assays. However, these molecular assays allow only the endpoint (extracellular) detection and use labels such as fluorometric, colorimetric and radioactive, which increase chances of uncertainty in detection. Additionally, they are time, labor and cost intensive. These technical limitations are unfavorable towards the development of a biosensor technology for continuous detection of toxins. Federal agencies including the Departments of Homeland Security, Agriculture, Defense and others have urged the development of a detect-to-protect class of advanced biosensors, which enable environmental surveillance of toxins in resource-limited settings. In this study a Surface-Enhanced Raman Spectroscopy (SERS) immunosensor, aka a SERS-linked immunosensor assay (SLISA), has been developed. Colloidal silver nanoparticles (Ag NPs) were used to design a flexible SERS immunosensor. The SLISA proof-of-concept biosensor was validated by the measurement of a dose dependent expression of RAD54 and HSP70 proteins in response to H2O2 and UV. A prototype microchip, best suited for SERS acquisition, was fabricated using an on-chip SLISA to detect RAD54 expression in response to H2O2. A dose-response relationship between H2O2 and RAD54 is established and correlated with EPA databases, which are established for human health risk assessment in the events of chemical exposure. SLISA outperformed ELISA by allowing RISE (rapid, inexpensive, simple and effective) detection of proteins within 2 hours and 3 steps. It did not require any label and provided qualitative information on antigen-antibody binding. SLISA can easily be translated to a portable assay using a handheld Raman spectrometer and it can be used in resource-limited settings. Additionally, this is the first report to deliver Ag NPs using TATHA2, a fusogenic peptide with cell permeability and endosomal rupture release properties, for rapid and high levels of Ag NPs uptake into yeast without significant toxicity, prerequisites for the development of the first intracellular SERS immunosensor.

Pre-diabetes is a serious health problem in the United States. Distinguished by plasma glucose levels that are above the normal threshold, patients with pre-diabetes are 10 times more likely to develop type 2 diabetes. Patients with pre-diabetes suffer the same complications as patients with diabetes including diabetic retinopathy, nephropathy, and microalbuminuria. There is considerable evidence to support the idea that early identification and aggressive treatment of pre-diabetes has the potential to delay disease progression. The American Diabetes Association’s clinical practice guideline recommends management of with lifestyle modification and metformin for patients who are at risk for developing type 2 diabetes. The purpose of this project was to evaluate the implementation of the 2012 ADA clinical practice guidelines regarding the management of patients with pre-diabetes by the health care providers at a volunteer-run clinic located in a large metropolitan area in the southeastern United States. This study, even with a small sample size (n=26) revealed that the providers at the clinic had not implemented the 2012 ADA clinical practice guidelines. Clinical practice guidelines promote health care interventions that have proven benefits and improve the consistency of care provided to patients. The greatest benefits of implementing clinical practice guidelines for patients with pre-diabetes are early diagnosis and aggressive disease management. This would improve patient outcomes and in the long run, decrease the cost of medical care.

Sepsis is the leading cause of death among critically ill patients and the 10th most common cause of death overall in the United States. The mortality rates increase with severity of the disease, ranging from 15% for sepsis to 60% for septic shock. Patient with sepsis can present varied clinical symptoms depending on the personal predisposition, causal microorganism, organ system involved, and disease severity. To facilitate sepsis diagnosis, the first sepsis consensus definitions was published in 1991 and then updated in 2001. Early recognition of a sepsis patient followed with timely and appropriate treatment and management strategies have been shown to significantly reduce sepsis-related mortality, and allows care to be provided at lower costs. Despite the rapid progress in the knowledge of pathophysiological mechanisms of sepsis and its treatment in the last two decades, identifying patient with sepsis and therapeutic approaches to sepsis and its complications remains challenging to critical care clinicians. Hence, the objectives of this thesis were to 1) evaluate the test characteristics of the two sepsis consensus definitions and delineate the differences in patient profile among patients meeting or not meeting sepsis definitions; 2) determine the relationship between the changes in several physiological parameters before sepsis onset and sepsis, and to determine whether these parameters could be used to identify sepsis in critically ill adults; 3) evaluate the effect of corticosteroids therapy on patient mortality. Data used in this thesis were prospectively collected from an electronic medical record system for all the adult patients admitted into the seven critical care units (ICUs) in a tertiary medical center. Besides analyzing data at the ICU stay level, we investigated patient information in various time frames, including 24-hour, 12-hour, and 6-hour time windows. In the first study of this thesis, the 1991 sepsis definition was found to have a high sensitivity of 94.6%, but a low specificity of 61.0%. The 2001 sepsis definition had a slightly increased sensitivity but a decreased specificity, which was 96.9% and 58.3%, respectively. The areas under the ROC curve for the two consensus definitions were similar, but less than optimal. The sensitivity and area under the ROC curve of both definitions were lower at the 24-hour time window level than those of the unit stay level, though the specificity increased slightly. At the time window level, the 1991 definitions performed slightly better than the 2001 definition. In the second study, minimum systolic blood pressure performed the best, followed by maximum respiratory rate in discriminating sepsis patients from SIRS patients. Maximum heart rate and maximum respiratory rate can differentiate sepsis patients from non-SIRS patients fairly well. The area under ROC of the combination of five physiological parameters was 0.74 and 0.90 for comparing sepsis to non-infectious SIRS patients and comparing sepsis to non-SIRS patients, respectively. Parameters typically performed better in 24-hour windows compared to 6-hour or 12-hour windows. In the third study, significantly increased hospital mortality and ICU mortality were observed in the group treated with low-dose corticosteroids than the control group based on the propensity score matched comparisons, and multivariate logistic regression analyses after adjustment for propensity score alone, covariates, or propensity score (in deciles) and covariates. This thesis advances the existing knowledge by systemically evaluating the test characteristics for the 1991 and 2001 sepsis consensus definitions, delineating physiological signs and symptoms of deterioration in the preceding 24 hours prior to sepsis onset, assessing the prediction performances of single or combined physiological parameters, and examining the use of corticosteroids treatment and survival among septic shock patients. In addition, this thesis sets an innovative example on how to use data from electronic medical records as these surveillance systems are becoming increasingly popular. The results of these studies suggest that a more parsimonious set of definitional criteria for sepsis diagnosis are needed to improve sepsis case identification. In addition, continuously monitored physiological parameters could help to identify patients who show signs of deterioration prior to developing sepsis. Last but not least, caution should be used when considering a recommendation on the use of low dose corticosteroids in clinical practice guidelines for the management of sepsis.

Extracellular vesicles (EVs), exosomes and microvesicles, transfer endogenous RNAs between neurons over short and long distances. We have explored EVs for siRNA delivery to brain. (1) We optimized siRNA chemical modifications and siRNA conjugation to lipids for EV-mediated delivery. (2) We developed a GMP-compatible, scalable method to manufacture active EVs in bulk. (3) We characterized lipid and protein content of EVs in detail. (4) We established how protein and lipid composition relates to siRNA delivering activity of EVs, and we reverse engineered natural exosomes (small EVs) into artificial exosomes based on these data. We established that cholesterol-conjugated siRNAs passively associate to EV membrane and can be productively delivered to target neurons. We extensively characterized this loading process and optimized exosome-to-siRNA ratios for loading. We found that chemical stabilization of 5'-phosphate with 5'-E-vinylphosphonate and chemical stabilization of all nucleotides with 2'-O-methyl and 2'-fluoro increases the accumulation of siRNA and the level of mRNA silencing in target cells. Therefore, we recommend using fully modified siRNAs for lipid-mediated loading to EVs. Later, we identified that α-tocopherol-succinate (vitamin E) conjugation to siRNA increases productive loading to exosomes compared to originally described cholesterol. Low EV yield has been a rate-limiting factor in preclinical development of the EV technology. We developed a scalable EV manufacturing process based on three-dimensional, xenofree culture of mesenchymal stem cells and concentration of EVs from conditioned media using tangential flow filtration. This process yields exosomes more efficient at siRNA delivery than exosomes isolated via differential ultracentrifugation from two-dimensional cultures of the same cells. In-depth characterization of EV content is required for quality control of EV preparations as well as understanding composition–activity relationship of EVs. We have generated mass-spectrometry data on more than 3000 proteins and more than 2000 lipid species detected in exosomes (small EVs) and microvesicles (large EVs) isolated from five different producer cells: two cell lines (U87 and Huh7) and three mesenchymal stem cell types (derived from bone marrow, adipose tissue and umbilical cord Wharton’s jelly). These data represent an indispensable resource for the community. Furthermore, relating composition change to activity change of EVs isolated from cells upon serum deprivation allowed us to identify essential components of siRNA-delivering exosomes. Based on these data we reverse engineered natural exosomes into artificial exosomes consisting of dioleoyl-phosphatidylcholine, cholesterol, dilysocardiolipin, Rab7, AHSG and Desmoplakin. These artificial exosomes reproduced efficient siRNA delivery of natural exosomes both in vitro and in vivo. Artificial exosomes may facilitate manufacturing, quality control and cargo loading challenge that currently impede the therapeutic EV field.

Effective pain management and time to treatment is essential in patient care. Despite scientific evidence supporting the need to treat pain and an emphasis on addressing pain as a priority, pain management continues to be an unresolved issue. As a member of the health care team, nurses are integral to optimal pain management. Currently, nursing schools have limited innovative or alternative methods for teaching pain assessment and management. Simulation in nursing education provides a unique opportunity to expose students to realistic patient situations and allow them to learn and make mistakes without causing harm. However, modern low- and high-fidelity simulation technology is unable to display emotion, pain, or any facial expression. This limits training and education of conditions that may partially rely on the identification of symptoms based on the alteration of facial appearance, such as pain or stroke. This research explored student nurses’ perception of new technology that displayed computer-generated faces, each expressing varying degrees of physical expressions of pain. A total of 15 nursing students participated in the study. Students were asked to interpret the level of pain in four sequential faces using a numeric rating scale of 0-10, with 0 indicating no pain, and 10 the most severe pain possible. After scoring the faces, students were asked to answer four open-ended questions addressing the technology. Results of the study indicate a majority of nursing students believe the technology should be implemented into nursing curriculum and interacting with the projected faces was more beneficial than traditional teaching methods. Eventually, the potential for increased identification of conditions requiring observation of subtle facial changes will be explored.

In this research, a proton exchange membrane fuel cell (PEMFC) sensor was investigated for specific detection of volatile organic compounds (VOCs) for point-of-care (POC) diagnosis of the physiological conditions of humans. A PEMFC is an electrochemical transducer that converts chemical energy into electrical energy. A Redox reaction takes place at its electrodes whereas the volatile biomolecules (e.g. ethanol) are oxidized at the anode and ambient oxygen is reduced at the cathode. The compounds which were the focus of this investigation were ethanol (C2H5OH) and isoflurane (C3H2ClF5O), but theoretically, the sensor is not limited to only those VOCs given proper calibration. Detection in biosensing, which needs to be carried out in a controlled system, becomes complex in a multivariate environment. Major limitations of all types of biosensors would include poor selectivity, drifting, overlapping, and degradation of signals. Specific detection of VOCs in multi-dimensional environments is also a challenge in fuel cell sensing. Humidity, temperature, and the presence of other analytes interfere with the functionality of the fuel cell and provide false readings. Hence, accurate and precise quantification of VOC(s) and calibration are the major challenges when using PEMFC biosensor. To resolve this problem, a statistical model was derived for the calibration of PEMFC employing multivariate analysis, such as the “Principal Component Regression (PCR)” method for the sensing of VOC(s). PCR can correlate larger data sets and provides an accurate fitting between a known and an unknown data set. PCR improves calibration for multivariate conditions as compared to the overlapping signals obtained when using linear (univariate) regression models. Results show that this biosensor investigated has a 75% accuracy improvement over the commercial alcohol breathalyzer used in this study when detecting ethanol. When detecting isoflurane, this sensor has an average deviation in the steady-state response of ~14.29% from the gold-standard infrared spectroscopy system used in hospital operating theaters. The significance of this research lies in its versatility in dealing with the existing challenge of the accuracy and precision of the calibration of the PEMFC sensor. Also, this research may improve the diagnosis of several diseases through the detection of concerned biomarkers.