DEVELOPMENT OF INFRARED SPECTROSCOPIC METHODS FOR ASSESSMENT OF EXTRACELLULAR MATRIX CHANGES IN CARDIOVASCULAR DISEASES

Extracellular matrix (ECM) is a key component and regulator of many biological tissues. Several cardiovascular pathologies are associated with significant changes in the composition of the matrix. Better understanding of these pathologies and the physiological phenomenon behind their development depends on reliable methods that can measure and characterize ECM content and structure. In this dissertation, infrared spectroscopic methodologies are developed to study the changes in extracellular matrix of cardiovascular tissue in two cardiovascular pathologies; myocardial infarction and abdominal aortic aneurysm. The specific aims of this dissertation were: 1. To develop a Fourier transform infrared imaging spectroscopy (FT-IRIS) methodology for creating distribution maps of collagen in remodeled cardiac tissue sections after myocardial infarction, and to quantitatively compare maps created by FT-IRIS with conventional staining techniques. 2. To develop an FT-IRIS method to assess elastin and collagen composition in the aortic wall. This will be accomplished using ex vivo animal aorta samples, where the primary ECM components of the wall will be systematically enzymatically degraded. 3. To apply the newly developed FTIR imaging methodology to evaluate changes in the primary ECM components (collagen and elastin) in the wall of human AAA tissues. The infrared absorbance band centered at 1338 cm-1, was used to map collagen deposition across heart tissue sections of a rat model of myocardial infarction, and was correlated strongly in the size of the scar (R=0.93) and local intensity of collagen deposition (R=0.86). In enzymatically degraded pig aorta samples, as a model of ECM degradation in abdominal aortic aneurysm (AAA), partial least squares (PLS) models were created to predict collagen and elastin content in aorta based on collected FTIR spectra and biochemically measured values. PLS models based on FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (R2=0.90 and 0.70 respectively). Elastin content prediction from IFOP spectra was successful through a PLS regression model with high correlation (R2=0.81). The PLS regression coefficient from the FT-IRIS models were used to map collagen and elastin human AAA biopsy tissue sections, creating a similar map of each component compared to histologically stained images. The mean value of collagen deposition in each tissue was calculated for 13 pairs of AAA samples where stress had been calculated using finite element modeling. In most pairs with stress values higher than 5 N/m2, collagen content was lower in the sample with higher stress value. Collagen maturity had a weak negative correlation (R=-0.35) with collagen content in these samples. These results confirm that infrared spectroscopy is a powerful tool that can be applied to replace or complement conventional methods such as histology and biochemical analysis to characterize ECM components in cardiovascular tissues. Furthermore, infrared spectroscopy has the potential for translation to a clinical environment to examine ECM changes in aorta in a minimally invasive fashion using fiber optic technology.Mechanical Engineerin

Development of infrared spectroscopic methods for assessment of extracellular matrix changes in cardiovascular diseases

Extracellular matrix (ECM) is a key component and regulator of many biological tissues. Several cardiovascular pathologies are associated with significant changes in the composition of the matrix. Better understanding of these pathologies and the physiological phenomenon behind their development depends on reliable methods that can measure and characterize ECM content and structure. In this dissertation, infrared spectroscopic methodologies are developed to study the changes in extracellular matrix of cardiovascular tissue in two cardiovascular pathologies; myocardial infarction and abdominal aortic aneurysm. The specific aims of this dissertation were: 1. To develop a Fourier transform infrared imaging spectroscopy (FT-IRIS) methodology for creating distribution maps of collagen in remodeled cardiac tissue sections after myocardial infarction, and to quantitatively compare maps created by FT-IRIS with conventional staining techniques. 2. To develop an FT-IRIS method to assess elastin and collagen composition in the aortic wall. This will be accomplished using ex vivo animal aorta samples, where the primary ECM components of the wall will be systematically enzymatically degraded. 3. To apply the newly developed FTIR imaging methodology to evaluate changes in the primary ECM components (collagen and elastin) in the wall of human AAA tissues. The infrared absorbance band centered at 1338 cm-1, was used to map collagen deposition across heart tissue sections of a rat model of myocardial infarction, and was correlated strongly in the size of the scar (R=0.93) and local intensity of collagen deposition (R=0.86). In enzymatically degraded pig aorta samples, as a model of ECM degradation in abdominal aortic aneurysm (AAA), partial least squares (PLS) models were created to predict collagen and elastin content in aorta based on collected FTIR spectra and biochemically measured values. PLS models based on FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (R2=0.90 and 0.70 respectively). Elastin content prediction from IFOP spectra was successful through a PLS regression model with high correlation (R2=0.81). The PLS regression coefficient from the FT-IRIS models were used to map collagen and elastin human AAA biopsy tissue sections, creating a similar map of each component compared to histologically stained images. The mean value of collagen deposition in each tissue was calculated for 13 pairs of AAA samples where stress had been calculated using finite element modeling. In most pairs with stress values higher than 5 N/m2, collagen content was lower in the sample with higher stress value. Collagen maturity had a weak negative correlation (R=-0.35) with collagen content in these samples. These results confirm that infrared spectroscopy is a powerful tool that can be applied to replace or complement conventional methods such as histology and biochemical analysis to characterize ECM components in cardiovascular tissues. Furthermore, infrared spectroscopy has the potential for translation to a clinical environment to examine ECM changes in aorta in a minimally invasive fashion using fiber optic technology

Fourier transform infrared spectroscopic imaging of cardiac tissue to detect collagen deposition after myocardial infarction

Myocardial infarction often leads to an increase in deposition of fibrillar collagen. Detection and characterization of this cardiac fibrosis is of great interest to investigators and clinicians. Motivated by the significant limitations of conventional staining techniques to visualize collagen deposition in cardiac tissue sections, we have developed a Fourier transform infrared imaging spectroscopy (FT-IRIS) methodology for collagen assessment. The infrared absorbance band centered at [Formula: see text] , which arises from collagen amino acid side chain vibrations, was used to map collagen deposition across heart tissue sections of a rat model of myocardial infarction, and was compared to conventional staining techniques. Comparison of the size of the collagen scar in heart tissue sections as measured with this methodology and that of trichrome staining showed a strong correlation ([Formula: see text]). A Pearson correlation model between local intensity values in FT-IRIS and immuno-histochemical staining of collagen type I also showed a strong correlation ([Formula: see text]). We demonstrate that FT-IRIS methodology can be utilized to visualize cardiac collagen deposition. In addition, given that vibrational spectroscopic data on proteins reflect molecular features, it also has the potential to provide additional information about the molecular structure of cardiac extracellular matrix proteins and their alterations

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

Aims: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. Methods: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. Results: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. Conclusions: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset