Scholarly Works, Statistics

Permanent URI for this collection

https://hdl.handle.net/10919/24334
Research articles, presentations, and other scholarship

Browse

Browsing Scholarly Works, Statistics by Department "Chemical Engineering"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer
    Huttanus, Herbert M.; Vu, Tommy; Guruli, Georgi; Tracey, Andrew; Carswell, William; Said, Neveen; Du, Pang; Parkinson, Bing G.; Orlando, Giuseppe; Robertson, John L.; Senger, Ryan S. (2020-08-21)
    Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix (TM)) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix(TM)screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix (TM) screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine(TM). Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix(TM)screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix(TM)was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix(TM)models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix(TM)screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.
  • Thumbnail Image
    Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix)
    Senger, Ryan S.; Kavuru, Varun; Sullivan, Meaghan; Gouldin, Austin; Lundgren, Stephanie; Merrifield, Kristen; Steen, Caitlin; Baker, Emily; Vu, Tommy; Agnor, Ben; Martinez, Gabrielle; Coogan, Hannah; Carswell, William; Karageorge, Lampros; Dev, Devasmita; Du, Pang; Sklar, Allan; Orlando, Giuseppe; Pirkle, James, Jr.; Robertson, John L. (PLOS, 2019-09-27)
    Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.
  • Thumbnail Image
    Transcriptomic Analysis of Hepatic Cells in Multicellular Organotypic Liver Models
    Tegge, Allison N.; Rodrigues, Richard R.; Larkin, Adam L.; Vu, Lucas T.; Murali, T. M.; Rajagopalan, Padmavathy (Springer Nature, 2018-07-27)
    Liver homeostasis requires the presence of both parenchymal and non-parenchymal cells (NPCs). However, systems biology studies of the liver have primarily focused on hepatocytes. Using an organotypic three-dimensional (3D) hepatic culture, we report the first transcriptomic study of liver sinusoidal endothelial cells (LSECs) and Kupffer cells (KCs) cultured with hepatocytes. Through computational pathway and interaction network analyses, we demonstrate that hepatocytes, LSECs and KCs have distinct expression profiles and functional characteristics. Our results show that LSECs in the presence of KCs exhibit decreased expression of focal adhesion kinase (FAK) signaling, a pathway linked to LSEC dedifferentiation. We report the novel result that peroxisome proliferator-activated receptor alpha (PPAR alpha) is transcribed in LSECs. The expression of downstream processes corroborates active PPAR alpha signaling in LSECs. We uncover transcriptional evidence in LSECs for a feedback mechanism between PPAR alpha and farnesoid X-activated receptor (FXR) that maintains bile acid homeostasis; previously, this feedback was known occur only in HepG2 cells. We demonstrate that KCs in 3D liver models display expression patterns consistent with an anti-inflammatory phenotype when compared to monocultures. These results highlight the distinct roles of LSECs and KCs in maintaining liver function and emphasize the need for additional mechanistic studies of NPCs in addition to hepatocytes in liver-mimetic microenvironments.