Abstract: Chronic Kidney Disease (CKD) is characterized by the progressive loss of renal function which eventually leads to End Stage Renal Disease (ESRD). CKD affects roughly 30 million Americans, costs billions of dollars in healthcare spending annually, and leaves thousands of patients reliant on burdensome dialysis treatments while waiting for a transplant. Fortunately, CKD may be controllable if diagnosed early in the disease progression. RAMETRIX™ is a novel public health screening technology for early diagnosis and detection of CKD. This technology uses Raman spectroscopy and chemometrics to analyze the molecular composition of urine and other biological fluids. RAMETRIX™ is a fast, non-invasive, accurate, and inexpensive diagnostic tool that can revolutionize the way healthcare providers detect and treat CKD. The RAMETRIX™ AutoScanner is a system enabling more efficient sample processing in large-scale settings such as hospitals and medical laboratories.

Abstract: A system and method for monitoring the health of dialysis patients with Raman spectroscopy measurements of one or more target analytes is described. The methods include irradiating one or more fluids of interest with light to produce one or more spectrum and detecting the spectrum with a detector. The fluids of interest are preferably those related to dialysis, including hemodialysis and peritoneal dialysis. In a preferred embodiment, the fluids are irradiated with monochromatic light, and one or more Raman spectra are detected as a result of the irradiation. The fluids may be irradiated within the dialysis tubing itself, or removed from the dialysis tubing and irradiated in a separate chamber. The Raman spectra of one or more target analytes of a dialysis patient may be followed over time or compared to one or more reference spectra, thereby providing information on the health of dialysis patients.

Abstract: A system and method for monitoring the health of dialysis patients with Raman spectroscopy measurements of one or more target analytes is described. The methods include irradiating one or more fluids of interest with light to produce one or more spectrum and detecting the spectrum with a detector. The fluids of interest are preferably those related to dialysis, including hemodialysis and peritoneal dialysis. In a preferred embodiment, the fluids are irradiated with monochromatic light, and one or more Raman spectra are detected as a result of the irradiation. The fluids may be irradiated within the dialysis tubing itself, or removed from the dialysis tubing and irradiated in a separate chamber. The Raman spectra of one or more target analytes of a dialysis patient may be followed over time or compared to one or more reference spectra, thereby providing information on the health of dialysis patients.

Abstract: The present invention comprises methods of detecting and classifying COVID-19 disease using Raman spectra obtained from subject urine samples. Raman spectra from subject urine samples are analyzed using models prepared from reference Raman samples obtained from urine samples of individuals with and without COVID-19. The spectral fingerprints of urine from subjects with and without COVID-19 allow for identification of disease-associated changes in urine molecular composition.

Abstract: The present invention is a system to continuously monitor, in real-time, the small molecules being dialyzed during hemodialysis treatment using Raman spectroscopy and press control algorithms. By monitoring the treatment, the amount of water needed per dialysis treatment is drastically reduced by optimizing analyte saturation and removal of wastes. This will significantly conserve water and reduce the cost of dialysis treatments, possibly reducing the amount of time necessary for dialysis treatment, improving quality of life for patients during and after treatment, and reducing the costs of building new treatment centers as well as operating costs.

Abstract: The present invention is a system to continuously monitor, in real-time, the small molecules being dialyzed during hemodialysis treatment using Raman spectroscopy and press control algorithms. By monitoring the treatment, the amount of water needed per dialysis treatment is drastically reduced by optimizing analyte saturation and removal of wastes. This will significantly conserve water and reduce the cost of dialysis treatments, possibly reducing the amount of time necessary for dialysis treatment, improving quality of life for patients during and after treatment, and reducing the costs of building new treatment centers as well as operating costs.

Abstract: Chronic Kidney Disease (CKD) is characterized by the progressive loss of renal function which eventually leads to End Stage Renal Disease (ESRD). CKD affects roughly 30 million Americans, costs billions of dollars in healthcare spending annually, and leaves thousands of patients reliant on burdensome dialysis treatments while waiting for a transplant. Fortunately, CKD may be controllable if diagnosed early in the disease progression. RAMETRIX™ is a novel public health screening technology for early diagnosis and detection of CKD. This technology uses Raman spectroscopy and chemometrics to analyze the molecular composition of urine and other biological fluids. RAMETRIX™ is a fast, non-invasive, accurate, and inexpensive diagnostic tool that can revolutionize the way healthcare providers detect and treat CKD. The RAMETRIX™ AutoScanner is a system enabling more efficient sample processing in large-scale settings such as hospitals and medical laboratories.

Abstract: Baseline correction in Raman spectroscopy is a procedure that eliminates/reduces the background signals generated by residual Rayleigh scattering or fluorescence. Provided is a novel baseline correction procedure called the Iterative Smoothing-splines with Root Error Adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines, which are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain, to estimate the baseline. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, the present inventors show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

Abstract: Disease detection and characterization using computational analysis of Raman spectra is used to detect disease-specific multi-molecular patterns “spectral fingerprint” associated with specific diseases, cellular physiologic derangements, or altered metabolism from systemic reactions to disease. Comparison of the Raman spectral fingerprint of urine from subjects with specific diseases and those not (healthy persons) provides the means to identify key disease-associated changes in urine molecular composition. Methods include applying baseline correction to spectra of a desired wavenumber range e.g.

Abstract: A system and method for monitoring the health of dialysis patients with Raman spectroscopy measurements of one or more target analytes is described. The methods include irradiating one or more fluids of interest with light to produce one or more spectrum and detecting the spectrum with a detector. The fluids of interest are preferably those related to dialysis, including hemodialysis and peritoneal dialysis. In a preferred embodiment, the fluids are irradiated with monochromatic light, and one or more Raman spectra are detected as a result of the irradiation. The fluids may be irradiated within the dialysis tubing itself, or removed from the dialysis tubing and irradiated in a separate chamber. The Raman spectra of one or more target analytes of a dialysis patient may be followed over time or compared to one or more reference spectra, thereby providing information on the health of dialysis patients.