Andrew J. Berger

Multicomponent blood analysis by near-infrared Raman spectroscopy

We demonstrate the use of Raman spectroscopy to measure the concentration of many important constituents (analytes) in serum and whole blood samples at physiological concentration in vitro across a multipatient data set. A near-infrared (830-nm) diode laser generates Raman spectra that contain superpositions of Raman signals from different analytes. Calibrations for glucose, cholesterol, urea, and other analytes are developed by use of partial least-squares cross validation. We predict six analytes in serum with significant accuracy in a 66-patient data set, using 60-s spectra. The calibrations are shown to be fairly robust against system drift over the span of seven weeks. In whole blood, a preliminary analysis yields accurate predictions of some of the same analytes and also hematocrit. The results hold promise for potential medical applications.

Direct characterization and removal of interfering absorption trends in two-layer turbid media

We propose a method to isolate absorption trends confined to the lower layer of a two-layer turbid medium, as is desired in near-infrared spectroscopy (NIRS) of cerebral hemodynamics. Several two-layer Monte Carlo simulations of NIRS time series were generated using a physiologically relevant range of optical properties and varying the absorption coefficients due to bottom-layer, top-layer, and/or global fluctuations. Initial results showed that by measuring absorption trends at two source-detector separations and performing a least-squares fit of one to the other, processed signals strongly resemble the simulated bottom-layer absorption properties. Through this approach, it was demonstrated that fitting coefficients can be estimated within less than +/- 2% of the ideal value without any a priori knowledge of the optical properties present in the model. An analytical approximation for the least-squares coefficient provides physical insight into the nature of errors and suggests ways to reduce them.

Feasibility of measuring blood glucose concentration by near-infrared Raman spectroscopy

We report the determinations of glucose concentrations in human whole blood samples made using near-infrared Raman spectroscopy. Raman spectra of blood samples with above-physiological levels of glucose were acquired for 5 min through the wall of a cuvette via fiber optics. Partial least squares analysis was used to predict glucose concentrations in the samples. A root mean squared prediction error of 3.6 mM glucose was achieved with a correlation coefficient of 0.99 between reference and predicted values. This result is the first step in evaluating the potential of near-infrared Raman spectroscopy to perform blood glucose measurement with clinical accuracy. The technique is capable of measuring the concentration of other Raman-active blood constituents; as an example, bicarbonate was also measured. The method could eventually be useful for direct measurement of tissue analytes.

Measurement of layer-like hemodynamic trends in scalp and cortex: implications for physiological baseline suppression in functional near-infrared spectroscopy

A multidetector, continuous wave, near-infrared spectroscopy (NIRS) system is developed to examine whether the hemodynamics of the scalp and brain in adults contain significant layer-like hemodynamic trends. NIRS measurements are made using contrasting geometries, one with four detectors equidistant from a source 33 mm away, and one with detectors collinear with the source (5 to 33 mm away). When NIRS time series are acquired over the prefrontal cortex from resting adults using both geometries, variations among the time series are consistent with a substantially homogeneous two-layer model (p<0.001) and inconsistent with one dominated by heterogeneities. Additionally, when time series measured 5 mm from the source are subtracted from corresponding 33-mm signals via a least-squares algorithm, 60% of the hemoglobin changes are on average removed. These results suggest that hemodynamic trends present in the scalp can contribute significantly to NIRS measurements, and that attempts to reduce this noise by subtracting a simultaneous near-channel measurement using a two-layer model are justified. Such subtractions are then performed on NIRS measurements from two stimulus protocols. For systemic stimulations (Valsalva maneuver), the subtraction cancels the hemodynamic response, as desired. For localized stimulation of the occipital lobe (viewing a flickering pattern), the subtraction isolated a stimulus-correlated hemodynamic feature from background noise.

Rapid, noninvasive concentration measurements of aqueous biological analytes by near-infrared Raman spectroscopy

Accurate concentration measurements of glucose, lactic acid, and creatinine in saline solution have beena chieved with near-IR Raman spectroscopy and a partial least-squares analysis. The Raman spectra were acquired remotely through optical fibers. A root-mean-squared prediction error of 1.2 mM for glucose concentration was achieved in 100 s. Concentrations of other analytes were predicted with similar accuracy.

Brain Specificity of Diffuse Optical Imaging: Improvements from Superficial Signal Regression and Tomography

Functional near infrared spectroscopy (fNIRS) is a portable monitor of cerebral hemodynamics with wide clinical potential. However, in fNIRS, the vascular signal from the brain is often obscured by vascular signals present in the scalp and skull. In this paper, we evaluate two methods for improving in vivo data from adult human subjects through the use of high-density diffuse optical tomography (DOT). First, we test whether we can extend superficial regression methods (which utilize the multiple source–detector pair separations) from sparse optode arrays to application with DOT imaging arrays. In order to accomplish this goal, we modify the method to remove physiological artifacts from deeper sampling channels using an average of shallow measurements. Second, DOT provides three-dimensional image reconstructions and should explicitly separate different tissue layers. We test whether DOTs depth-sectioning can completely remove superficial physiological artifacts. Herein, we assess improvements in signal quality and reproducibility due to these methods using a well-characterized visual paradigm and our high-density DOT system. Both approaches remove noise from the data, resulting in cleaner imaging and more consistent hemodynamic responses. Additionally, the two methods act synergistically, with greater improvements when the approaches are used together.

Chemical concentration measurement in blood serum and urine samples using liquid-core optical fiber Raman spectroscopy

We report measurements of chemical concentrations in clinical blood serum and urine samples using liquid-core optical fiber (LCOF) Raman spectroscopy to increase the collected signal strength. Both Raman and absorption spectra were acquired in the near-infrared region using the LCOF geometry. Spectra of 71 blood serum and 61 urine samples were regressed via partial least squares against reference analyzer values. Significant correlation was found between predicted and reference concentrations for 13 chemicals. Using absorption data to normalize the LCOF enhancement made the results more accurate. The experimental geometry is well suited for high-volume and automated chemical analysis of clear biofluids.

Compound parabolic concentrator probe for efficient light collection in spectroscopy of biological tissue

We describe a compound parabolic concentrator (CPC)-based probe for enhanced signal collection in the spectroscopy of biological tissues. Theoretical considerations governing signal enhancement compared with conventional collection methods are given. A ray-tracing program was used to analyze the throughput of CPCs with shape deviations and surface imperfections. A modified CPC shape with 99% throughput was discovered. A 4.4-mm-long CPC was manufactured and incorporated into an optical fiber-based near-infrared Raman spectrometer system. For human tissue samples, light collection was enhanced by a factor of 7 compared with collection with 0.29-NA optical fibers.

Heavy metal lead exposure, osteoporotic-like phenotype in an animal model, and depression of Wnt signaling.

Background: Exposure to lead (Pb) from environmental and industrial sources remains an overlooked serious public health risk. Elucidating the effect of Pb on bone cell function is therefore critical for understanding its risk associated with diseases of low bone mass. Objectives: We tested the hypothesis that Pb negatively affects bone mass. We also assessed the underlying mechanisms of Pb on bone signaling pathways. Methods: We used a model of low-level Pb exposure in a rodent beginning before conception and continuing over 18 months. We characterized the effect of Pb on bone quality using dual-energy X-ray absorptiometry (DXA), micro-computed tomography, Raman spectroscopy, and histology. We assessed the effect of Pb on bone and adipocyte formation by mineral deposition, lipid droplet formation, and Western blot and RNA analysis. Results: Pb-exposed animals had decreased bone mass that resulted in bones that were more susceptible to fracture. Pb decreased osteoblastic cell number leading to a depression of bone formation. Accompanying this, Pb exposure elevated sclerostin protein levels in the skeleton, and correspondingly reduced levels of β-catenin and Runx2 in stromal precursor cells. Pb also increased skeletal expression of peroxisome proliferator-activated receptor-γ (PPAR-γ). These results indicate a shift in mesenchymal differentiation wherein Pb promoted enhanced adipogenesis and decreased osteoblastogenesis. Substantial differences in bone marrow composition were observed, highlighted by an increase in adipocytes. Conclusions: The disruption Pb has on bone mass and bone homeostasis is principally explained by inhibition of the Wnt/β-catenin pathway, which may provide a molecular basis for novel therapeutic strategies to combat Pb-induced bone pathologies.

Determination of Ideal Offset for Spatially Offset Raman Spectroscopy

A key design parameter in spatially offset Raman spectroscopy (SORS) is the choice of offset distance between the illumination and collection areas. To investigate this choice, we performed SORS measurements on a simple two-layer chemical phantom. We show that while the SORS ratio, or the ratio of signal from the bottom layer to the top layer, monotonically increases with spatial offset, the signal-to-noise ratio (SNR) does not. Specifically, we show that there exists a specific spatial offset that yields the best SNR for signal originating in the bottom layer of a two-layer sample. We also show that this SNR-optimal offset depends upon the strength of the particular Raman band. This work presents the considerations that should be taken into account when designing optical probes for use in SORS.