Department of Chemical Engineering

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Browsing Department of Chemical Engineering by Subject "4.1 Discovery and preclinical testing of markers and technologies"

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    Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis
    Robertson, John L.; Senger, Ryan S.; Talty, Janine; Du, Pang; Sayed-Issa, Amr; Avellar, Maggie L.; Ngo, Lacy T.; Gomez de la Espriella, Mariana; Fazili, Tasaduq N.; Jackson-Akers, Jasmine Y.; Guruli, Georgi; Orlando, Giuseppe (PLOS, 2022-07-01)
    We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59–84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.
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    Profiling renal dysfunction using Raman chemometric urinalysis, with special reference to COVID19, lupus nephritis, and diabetic nephropathy
    Robertson, John L.; Issa, Amr Sayed; Gomez, Mariana; Sullivan, Kathleen; Senger, Ryan S. (Knowledge Enterprise Journals, 2023-09-30)
    Background: Many systemic and urinary tract diseases alter renal structure and function, including changing the composition of urine. While routine urinalysis (physical properties, sediment evaluation, urine chemistry analytes) is useful in screening, it has limitations on separating disease processes, structural changes, and functional abnormalities. Likewise, while many individual ‘biomarkers’ have been used to screen for disease, they have not met with widespread clinical adoption. The recent COVID19 Pandemic and the recognition of post-acute sequelae SARS-CoV-2 infection (PASC) have highlighted the need for rapid, scalable, economical, and accurate screening tools for managing disease. Aims: Validate a Raman spectroscopy-based screening technology for urine analysis that could be used for recognition and quantification of systemic and renal effects of acute and PASC COVID19 disease. Methods: One hundred ten (110) urine specimens were obtained from consented adults diagnosed with COVID19 disease by RT-PCR and/or proximate (household) contact With RT-PCR-confirmed COVID19 disease. Samples were analyzed using Raman chemometric urinalysis, a technology that detects hundreds of discrete chemicals in urine and applies computational comparison-machine learning to detect COVID19-associated molecular patterns (‘fingerprints’). Results: When compared with the urine multimolecular ‘fingerprints’ of healthy individuals and patients with known systemic diseases (diabetes mellitus, lupus) that alter renal structure and function, patients with acute and PASC COVID19 had unique ‘fingerprints’ indicative of alterations in renal function (i.e. – infection altered urine composition). Differences in disease severity (mild to severe) were reflected by different ‘fingerprints’ in urine. Roughly 20% of hospitalized patients developed a degree of renal dysfunction (decrements in eGFR) that were correlated with distinct changes in urine fingerprints. Conclusion: Raman chemometric urinalysis may be a useful tool in management of patients with COVID19 disease, particularly in detecting patients with evolving renal dysfunction for whom there should be attention to medication use and renal health restoration/preservation.