Targeting for stereotactic radiosurgical thalamotomy based on tremor treatment response.

OBJECTIVE: Stereotactic radiosurgery (SRS) treats severe, medically refractory essential tremor and tremor-dominant Parkinson disease. However, the optimal target for SRS treatment within the thalamic ventral intermediate nucleus (VIM) is not clearly defined. This work evaluates the precision of the physician-selected VIM target, and determines the optimal SRS target within the VIM by correlation between early responders and nonresponders. METHODS: Early responders and nonresponders were assessed retrospectively by Elements Basal Ganglia Atlas autocontouring of the VIM on the pre-SRS-treatment 1-mm slice thickness T1-weighted MRI and correlating the center of the post-SRS-treatment lesion. Using pre- and posttreatment diffusion tensor imaging, the fiber tracking package in the Elements software generated tremor-related tracts from autosegmented motor cortex, thalamus, red nucleus, and dentate nucleus. Autocontouring of the VIM was successful for all patients. RESULTS: Among 23 patients, physician-directed SRS targets had a medial-lateral target range from +2.5 mm to -2.0 mm from the VIM center. Relative to the VIM center, the SRS isocenter target was 0.7-0.9 mm lateral for 6 early responders and 0.9-1.1 mm medial for 4 nonresponders (p = 0.019), and without differences in the other dimensions: 0.2 mm posterior and 0.6 mm superior. Dose-volume histogram analyses for the VIM had no significant differences between responders and nonresponders between 20 Gy and 140 Gy, mean or maximum dose, and dose to small volumes. Tractography data was obtained for 4 patients. CONCLUSIONS: For tremor control in early responders, the Elements Basal Ganglia Atlas autocontour for the VIM provides the optimal SRS target location that is 0.7-0.9 mm lateral to the VIM center.

Adsorption Kinetics of Glycated Hemoglobin on Aptamer Microarrays with Antifouling Surface Modification.

Aptamers are oligonucleotides that bind with high affinity to target molecules of interest. One such target is glycated hemoglobin (gHb), a biomarker for assessing glycemic control and diabetes diagnosis. By the coupling of aptamers with surface plasmon resonance (SPR) sensing surfaces, a fast, reliable and inexpensive assay for gHb can be developed. In this study, we tested the affinity of SPR-sensing surfaces, composed of aptamers and antifouling self-assembled monolayers (SAMs), to hemoglobin (Hb) and gHb. First, we developed a gHb-targeted aptamer (GHA) through a modified Systematic Evolution of Ligands by EXponential (SELEX) enrichment process and tested its affinity to gHb using the Nano-Affi protocol. GHA was used to produce three distinct SAM-SPR-sensing surfaces: (Type-1) a SAM of GHA directly attached to a sensor surface; (Type-2) GHA attached to a SAM of 11-mercaptoundecanoic acid (11MUA) on a sensor surface; (Type-3) GHA attached to a binary SAM of 11MUA and 3,6-dioxa-8-mercaptooctan-1-ol (DMOL) on a sensor surface. Type-2 and Type-3 surfaces were characterized by cyclic voltammetry and electrochemical impedance spectroscopy to confirm that GHA bound to the underlying SAMs. The adsorption kinetics for Hb and gHb interacting with each SPR sensing surface were used to quantify their respective affinities. The Type-1 surface without antifouling modification had a dissociation constant ratio (KD,Hb/KD,gHb) of 9.7, as compared to 809.3 for the Type-3 surface, demonstrating a higher association of GHA to gHb for sensor surfaces with antifouling modifications than those without. The enhanced selectivity of GHA to gHb can likely be attributed to the inclusion of DMOL in the SAM-modified surface, which reduced interference from nonspecific adsorption of proteins. Results suggest that pairing aptamers with antifouling SAMs can significantly improve their target affinity, potentially allowing for the development of novel, low cost, and fast assays.

Selection and characterization of structure-switching DNA aptamers for the salivary peptide histatin 3.

In this study, single-stranded DNA aptamers that switch structural conformation upon binding to the salivary peptide histatin 3 have been reported for the first time. Histatin 3 is an antimicrobial peptide that possesses the capability of being a therapeutic agent against oral candidiasis and has recently been linked as a novel biomarker for acute stress. The aptamers were identified through a library immobilization version of an iterative in vitro process known as the Systematic Evolution of Ligands by EXponential enrichment (SELEX). Through the SELEX process, four unique aptamer candidates sharing a consensus sequence were identified. These selected sequences exhibited binding affinity and specificity to histatin 3 and in order to further characterize these aptamers, a direct format enzyme-linked aptamer sorbent assay (ELASA) was developed. The best performing candidate demonstrated an equilibrium dissociation constant (Kd) value of 1.97 ± 0.48 µM. These novel aptamers have the potential to lead to the further development of refined sensing assays and platforms for the detection and quantification of histatin 3 in human saliva and other biological media.

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Background: Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditions render maintenance of glycemic control very challenging in this setting. Materials and Methods: This study involved collection of continuous glucose monitoring (CGM) data, and other associated measures, from the electronic medical records of 127 patients for the first 72 h of ICU care who upon admission to the ICU had a diagnosis of type 1 (n = 8) or type 2 diabetes (n = 97) or a glucose value >150 mg/dL (n = 22). A neural network-based model was developed to predict a complete trajectory of glucose values up to 135 min ahead of time. Model accuracy was validated using data from 15 of the 127 patients who were not included in the model training set to simulate model performance in real-world health care settings. Results: Predictive models achieved an improved accuracy and performance compared with previous models that were reported by our research team. Model error, expressed as mean absolute difference percent, was 10.6% with respect to interstitial glucose values (CGM) and 15.9% with respect to serum blood glucose values collected 135 min in the future. A Clarke Error Grid Analysis of model predictions with respect to the reference CGM and blood glucose measurements revealed that >99% of model predictions could be regarded as clinically acceptable and would not lead to inaccurate insulin therapy or treatment recommendations. Conclusion: The noted clinical acceptability of these models illustrates their potential utility within a clinical decision support system to assist health care providers in the optimization of glycemic management in critical care patients.

Combination immunotherapy and radiotherapy causes an abscopal treatment response in a mouse model of castration resistant prostate cancer.

BACKGROUND: Prostate cancer is poorly responsive to immune checkpoint inhibition, yet a combination with radiotherapy may enhance the immune response. In this study, we combined radiotherapy with immune checkpoint inhibition (iRT) in a castration-resistant prostate cancer (CRPC) preclinical model. METHODS: Two Myc-CaP tumor grafts were established in each castrated FVB mouse. Anti-PD-1 or anti-PD-L1 antibodies were given and one graft was irradiated 20 Gy in 2 fractions. RESULTS: In CRPC, a significant increase in survival was found for radiation treatment combined with either anti-PD-1 or anti-PD-L1 compared to monotherapy. The median survival for anti-PD-L1 alone was 13 days compared to 30 days for iRT (p = 0.0003), and for anti-PD-1 alone was 21 days compared to 36 days for iRT (p = 0.0009). Additional treatment with anti-CD8 antibody blocked the survival effect. An abscopal treatment effect was observed for iRT in which the unirradiated graft responded similarly to the irradiated graft in the same mouse. At 21 days, the mean graft volume for anti-PD-1 alone was 2094 mm3 compared to iRT irradiated grafts 726 mm3 (p = 0.04) and unirradiated grafts 343 mm3 (p = 0.0066). At 17 days, the mean graft volume for anti-PD-L1 alone was 1754 mm3 compared to iRT irradiated grafts 284 mm3 (p = 0.04) and unirradiated grafts 556 mm3 (p = 0.21). Flow cytometry and immunohistochemistry identified CD8+ immune cell populations altered by combination treatment in grafts harvested at the peak effect of immunotherapy, 2-3 weeks after starting treatment. CONCLUSIONS: These data provide preclinical evidence for the use of iRT targeting PD-1 and PD-L1 in the treatment of CRPC. Immune checkpoint inhibition combined with radiotherapy treats CPRC with significant increases in median survival compared to drug alone: 70% longer for anti-PD-1 and 130% for anti-PD-L1, and with an abscopal treatment effect. PRECIS: Castration-resistant prostate cancer in a wild-type mouse model is successfully treated by X-ray radiotherapy combined with PD-1 or PD-L1 immune checkpoint inhibition, demonstrating significantly increased median overall survival and robust local and abscopal treatment responses, in part mediated by CD8 T-cells.

Bcl2-Expressing Quiescent Type B Neural Stem Cells in the Ventricular-Subventricular Zone Are Resistant to Concurrent Temozolomide/X-Irradiation.

The ventricular-subventricular zone (V-SVZ) of the mammalian brain is a site of adult neurogenesis. Within the V-SVZ reside type B neural stem cells (NSCs) and type A neuroblasts. The V-SVZ is also a primary site for very aggressive glioblastoma (GBM). Standard-of-care therapy for GBM consists of safe maximum resection, concurrent temozolomide (TMZ), and X-irradiation (XRT), followed by adjuvant TMZ therapy. The question of how this therapy impacts neurogenesis is not well understood and is of fundamental importance as normal tissue tolerance is a limiting factor. Here, we studied the effects of concurrent TMZ/XRT followed by adjuvant TMZ on type B stem cells and type A neuroblasts of the V-SVZ in C57BL/6 mice. We found that chemoradiation induced an apoptotic response in type A neuroblasts, as marked by cleavage of caspase 3, but not in NSCs, and that A cells within the V-SVZ were repopulated given sufficient recovery time. 53BP1 foci formation and resolution was used to assess the repair of DNA double-strand breaks. Remarkably, the repair was the same in type B and type A cells. While Bax expression was the same for type A or B cells, antiapoptotic Bcl2 and Mcl1 expression was significantly greater in NSCs. Thus, the resistance of type B NSCs to TMZ/XRT appears to be due, in part, to high basal expression of antiapoptotic proteins compared with type A cells. This preclinical research, demonstrating that murine NSCs residing in the V-SVZ are tolerant of standard chemoradiation therapy, supports a dose escalation strategy for treatment of GBM. Stem Cells 2019;37:1629-1639.

The Role of Nrf2 in the Response to Normal Tissue Radiation Injury.

The transcription factor Nrf2 is an important modulator of antioxidant and drug metabolism, carbohydrate and lipid metabolism, as well as heme and iron metabolism. Regulation of Nrf2 expression occurs transcriptionally and post-transcriptionally. Post-transcriptional regulation entails ubiquitination followed by proteasome-dependent degradation. Additionally, Nrf2-mediated gene expression is subject to negative regulation by ATF3, Bach1 and cMyc. Nrf2-mediated gene expression is an important regulator of a cell's response to radiation. Although a majority of studies have shown that Nrf2 deficient cells are radiosensitized and Nrf2 over expression confers radioresistance, Nrf2's role in mediating the radiation response of crypt cells is controversial. The Nrf2 activator CDDO attenuates radiation-mediated crypt injury, whereas intestinal crypts in Nrf2 null mice are radiation resistant. Further investigation is needed in order to define the relationship between Nrf2 and radiation sensitivity in Lgr5+ and Bmi1+ cells that regulate regeneration of crypt stem cells. In hematopoietic compartments Nrf2 promotes the survival of irradiated osteoblasts that support long-term hematopoietic stem cell (LT-HSC) niches. Loss of Nrf2 in LT-HSCs increases stem cell intrinsic radiosensitivity, with the consequence of lowering the LD5030. An Nrf2 deficiency drives LT-HSCs from a quiescent to a proliferative state. This results in hematopoietic exhaustion and reduced engraftment after myoablative irradiation. The question of whether induction of Nrf2 in LT-HSC enhances hematopoietic reconstitution after bone marrow transplantation is not yet resolved. Irradiation of the lung induces pulmonary pneumonitis and fibrosis. Loss of Nrf2 promotes TGF-ß/Smad signaling that induces ATF3 suppression of Nrf2-mediated target gene expression. This, in turn, results in elevated reactive oxygen species (ROS) and isolevuglandin adduction of protein that impairs collagen degradation, and may contribute to radiation-induced chronic cell injury. Loss of Nrf2 impairs ΔNp63 stem/progenitor cell mobilization after irradiation, while promoting alveolar type 2 cell epithelial-mesenchymal transitions into myofibroblasts. These studies identify Nrf2 as an important factor in the radiation response of normal tissue.

Development and characterization of erythrosine nanoparticles with potential for treating sinusitis using photodynamic therapy.

BACKGROUND: Antimicrobial therapy for sinusitis has been shown to reduce or eliminate pathologic bacteria associated with rhinosinusitis and improve the symptoms associated with the disease. However, the continuing rise in antibiotic resistance, the ongoing problem with patient compliance, and the intrinsic difficulty in eradication of biofilms complicates antibiotic therapy. The introduction of photodynamic antimicrobial therapy (PAT) using erythrosine, a photosensitizer, could eliminate the bacteria without inducing antibiotic resistance or even requiring daily dosing. In the present study, erythrosine nanoparticles were prepared using poly-lactic-co-glycolic acid (PLGA) and evaluated for their potential in PAT against Staphylococcus aureus cells. METHODS: PLGA nanoparticles of erythrosine were prepared by nanoprecipitation technique. Erythrosine nanoparticles were characterized for size, zeta potential, morphology and in vitro release. Qualitative and quantitative uptake studies of erythrosine nanoparticles were carried out in S. aureus cells. Photodynamic inactivation of S. aureus cells in the presence of erythrosine nanoparticles was investigated by colony forming unit assay. RESULTS: Nanoprecipitation technique resulted in nanoparticles with a mean diameter of 385nm and zeta potential of -9.36mV. Erythrosine was slowly released from nanoparticles over a period of 120h. The qualitative study using flow cytometry showed the ability of S. aureus cells to internalize erythrosine nanoparticles. Moreover, erythrosine nanoparticles exhibited a significantly higher uptake and antimicrobial efficacy compared to pure drug in S. aureus cells. CONCLUSION: In conclusion, erythrosine-loaded PLGA nanoparticles can be a potential long term drug delivery system for PAT and are useful for the eradication of S. aureus cells.

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.

A novel glucose biosensor using bi-enzyme incorporated with peptide nanotubes.

A novel amperometric glucose biosensor was developed using the bio-inspired peptide nanotube (PNT) as an encapsulation template for enzymes. Horseradish peroxidase (HRP) was encapsulated by the PNT and glucose oxidase (GO(x)) was co-immobilized with the PNT on a gold nanoparticle (AuNP)-modified electrode. A binary SAM of 3-mercaptopropionic acid (MPA) and 1-tetradecanethiol (TDT) was formed on the surface of the electrode to immobilize the PNT and GO(x). The resulting electrode appeared to provide the enzymes with a biocompatible nanoenvironment as it sustained the enhanced enzyme activity for an extended time and promoted possible direct electron transfer through the PNT to the electrode. Performance of the biosensor was evaluated in terms of its detection limit, sensitivity, pH, response time, selectivity, reproducibility, and stability in a lab setting. In addition the sensor was tested for real samples. The composite of AuNP-SAM-PNT/HRP-GO(x) to fabricate a sensor electrode in this study exhibited a linear response with glucose in the concentration range of 0.5-2.4mM with a R(2)-value of 0.994. A maximum sensitivity of 0.3 mA M(-1)and reproducibility (RSD) of 1.95% were demonstrated. The PNT-encapsulated enzyme showed its retention of >85% of the initial current response after one month of storage.

Development of a highly specific amine-terminated aptamer functionalized surface plasmon resonance biosensor for blood protein detection.

This paper presents a generally applicable approach for the highly specific detection of blood proteins. Thrombin and thrombin-binding aptamers are chosen for demonstration purposes. The sensor was prepared by immobilizing amine-terminated aptamers onto a gold modified surface using a two-step self-assembled monolayer (SAM) immobilization technique and the physical detection is performed using Surface Plasmon Resonance (SPR). The developed sensor has an optimal detectable range of 5-1000 nM and the results show the sensor has good reversibility, sensitivity and selectivity. Furthermore, the sensor shows the potential of being improved and standardized for direct detection of other blood proteins for clinical applications.

Noninvasive polarimetric-based glucose monitoring: an in vivo study.

BACKGROUND: Since 1990, there has been significant research devoted toward development of a noninvasive physiological glucose sensor. In this article, we report on the use of optical polarimetry for the noninvasive measurement of physiological glucose concentration in the anterior chamber of the eye of New Zealand white (NZW) rabbits. METHOD: Measurements were acquired using a custom-designed laser-based optical polarimetry system in a total of seven NZW rabbits anesthetized using an isoflurane-only anesthesia protocol. Aqueous humor-based polarimetric measurements were obtained by coupling light through the anterior chamber of the eye. Blood glucose levels were first stabilized and then altered with intravenous dextrose and insulin administration and measured every 3-5 min with a standard glucometer and intermittently with a YSI 2300 glucose analyzer. Acquired polarimetric glucose signals are calibrated to measured blood glucose concentration. RESULTS: Based on a total of 41 data points, Clarke error grid analysis indicated 93% in zone A, 7% in zone B, and 0% in zones C and D, with reference concentrations between 93 and 521 mg/dl. Errors in prediction are shown to be related to gross movement of the rabbit during the procedures, incurring time-varying corneal birefringence effects that directly affect the measured polarimetric signal. These effects can be compensated for with appropriate design modifications. CONCLUSIONS: An optical polarimetry technique was used for in vivo physiological glucose monitoring. The technique demonstrated provides a basis for the development of a noninvasive polarimetric glucose monitor for home, personal, or hospital use.

Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) technologies report measurements of interstitial glucose concentration every 5 min. CGM technologies have the potential to be utilized for prediction of prospective glucose concentrations with subsequent optimization of glycemic control. This article outlines a feed-forward neural network model (NNM) utilized for real-time prediction of glucose. METHODS: A feed-forward NNM was designed for real-time prediction of glucose in patients with diabetes implementing a prediction horizon of 75 min. Inputs to the NNM included CGM values, insulin dosages, metered glucose values, nutritional intake, lifestyle, and emotional factors. Performance of the NNM was assessed in 10 patients not included in the model training set. RESULTS: The NNM had a root mean squared error of 43.9 mg/dL and a mean absolute difference percentage of 22.1. The NNM routinely overestimates hypoglycemic extremes, which can be attributed to the limited number of hypoglycemic reactions in the model training set. The model predicts 88.6% of normal glucose concentrations (> 70 and < 180 mg/dL), 72.6% of hyperglycemia (≥ 180 mg/dL), and 2.1% of hypoglycemia (≤ 70 mg/dL). Clarke Error Grid Analysis of model predictions indicated that 92.3% of predictions could be regarded as clinically acceptable and not leading to adverse therapeutic direction. Of these predicted values, 62.3% and 30.0% were located within Zones A and B, respectively, of the error grid. CONCLUSIONS: Real-time prediction of glucose via the proposed NNM may provide a means of intelligent therapeutic guidance and direction.

Development of a neural network model for predicting glucose levels in a surgical critical care setting.

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia.

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

BACKGROUND: A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g., sleep-wake cycles and exercise), and emotional states (e.g., stress). The overall effect of these factors has not been fully quantified to determine the impact on subsequent glycemic trends. Recent advances in diabetes technology such as continuous glucose monitoring (CGM) provides significant sources of data, such that quantification may be possible. Depending on the CGM technology utilized, the sampling frequency ranges from 1-5 min. In this study, an intensive electronic diary documenting the factors previously described was created. This diary was utilized by 18 patients with insulin-dependent diabetes mellitus in conjunction with CGM. Utilizing this dataset, various neural network models were constructed to predict glucose in these diabetes patients while varying the predictive window from 50-180 min. The predictive capability of each neural network within the fully trained dataset was analyzed as well as the predictive capabilities of the neural networks on unseen data. METHODS: Neural network models were created using NeuroSolutions software with variable predictive windows of 50, 75, 100, 120, 150, and 180 min. Neural network models were trained using patient datasets ranging from 11-17 patients and evaluated on patient data not included in the neural network formulation. Performance analysis was completed for the neural network models using MATLAB. Performance measures include the calculation of the mean absolute difference percent overall and at hypoglycemic and hyperglycemic extremes, and the percentage of hypoglycemic and hyperglycemic occurrences were predicted. RESULTS: Overall, the neural network models perform adequately at predicting at normal (>70 and <180 mg/dl) and hyperglycemic ranges (> or =180 mg/dl); however, glucose concentrations in areas of hypoglycemia were commonly overestimated. One potential reason for the "high" predictions in areas of hypoglycemia is due to the minimal occurrences of hypoglycemic events within the training data. The entire 18-patient dataset (consisting of 18,400 glucose values) had a relatively low incidence of hypoglycemia (1460 CGM values < or =70 mg/dl), which corresponds to approximately 7.9% of the dataset. On the contrary, hyperglycemia comprised approximately 35.7% of the dataset (6560 CGM values >or =180 mg/dl), and euglycemic values allotted for 56.4% of the dataset (10,380 CGM values >70 and <180 mg/dl). Results further indicate that an increase in predictive window leads to a decrease in predictive accuracy of the neural network model. It is hypothesized that the underestimation of hyperglycemic extremes is due to the extension of the predictive window and the associated inability of the neural network to determine oscillations and trends in glycemia as well as the occurrence of other relevant input events such as lifestyle, emotional states, insulin dosages, and meals, which may occur within the predicted time window and may impact or change neural network weights. CONCLUSIONS: In this investigation, the feasibility of utilizing neural network models for the prediction of glucose using predictive windows ranging from 50-180 min is demonstrated. The predictive windows were chosen arbitrarily to cover a wide range; however, longer predictive windows were implemented to gain a predictive view of 120-180 min, which is very important for diabetes patients, specifically after meals and insulin dosages. Neural networks, such as those generated in this investigation, could be utilized in a semiclosed-loop device for guiding therapy in diabetes patients. Use of such a device may lead to better glycemic control and subsequent avoidance of complications.

Polarization-based diffuse reflectance imaging for noninvasive measurement of glucose.

BACKGROUND: The ability to measure glucose concentration through noninvasive approaches would impact the treatment of diabetes significantly. Polarization-based optical approaches have received considerable interest because of their potential medical applications. Glucose, a chiral molecule, has the ability to rotate the plane of linearly polarized light, commonly referred to as optical activity, as well as changing the refractive index of the media, which therefore affects the overall scattering coefficient in a given media. The magnitude of each effect is related to the concentration of glucose. Although most previous studies have reported on the use of polarimetry in the aqueous humor of the eye because of its nonscattering nature, one would also expect that glucose concentration could be measured in more turbid media such as tissue through a similar approach. This study investigated how each of these effects is correlated to glucose concentration in a physiological range for highly scattering biological media. METHODS: A custom-designed imaging polarimeter was used to image highly scattering Intralipid-based media containing different concentrations of glucose. Model formation and glucose prediction were performed through the use of partial least squares (PLS) regression. Further insight into the differences between polarization-based image measurements and encoding of glucose information was provided through the use of principal component analysis (PCA). RESULTS: When coupled with PLS regression, in vitro polarization measurements yielded highly correlated glucose predictions in both calibration and independent validation, 0.999 and 0.998, respectively. Through the use of PCA, it appears that the majority of the image-based signal yielding the most significant glucose information is attributable to changes in the overall scattering coefficient due to glucose concentration and, to a lesser degree, effects of optical activity. CONCLUSIONS: This study showed how polarimetric-based imaging coupled with PLS regression can be used to quantify glucose concentration in highly scattering media. Such methods may potentially be able to extend the use of noninvasive in vivo polarimetric measurements, normally acquired in the anterior chamber of the eye, to other preferred sensing sites such as the skin.

Determination of optical scattering properties in turbid media using Mueller matrix imaging.

A need exists for the continued development of diagnostic tools and methods capable of distinguishing and characterizing slight differences in the optical properties of tissues. We present a method to estimate the scattering coefficient contribution as a function of particle size in complex mixtures of polystyrene spheres. The experimental method we used is a Mueller matrix imaging approach. The Mueller matrix encodes the polarization-dependent properties of the sample and describes how a given sample will transform an incident light polarization state. A partial least-squares approach is used to form a model around a set of Mueller matrix image-based measurements to accurately predict the individual scattering coefficient contributions in phantoms containing 0.2, 0.5, 1, and 2 microm-diameter polystyrene spheres. The results show individual scattering coefficient contribution errors as low as 0.1585 cm(-1) can be achieved. In addition, it is shown how the scattering type (i.e., Rayleigh and Mie) is encoded within the Mueller matrix. Such methods may eventually lead to the development of improved diagnostic tools capable of characterizing and distinguishing between tissue abnormalities, such as superficial cancerous lesions from their benign counterparts.

Development of a real-time corneal birefringence compensated glucose sensing polarimeter.

BACKGROUND: As is well known, in order to optimally manage diabetes mellitus, monitoring blood glucose levels several times daily is recommended so appropriate actions can be taken to maintain these levels within a near-normal physiologic range. One technique that shows promise is the use of optical polarimetry. This technique has the potential to noninvasively measure physiological glucose levels in the eye that are correlated to blood glucose concentrations. To date, the main factor limiting in vivo polarimetric glucose measurements is corneal birefringence, which tends to mask the glucose signature. In this investigation, a method to compensate for the effects of corneal birefringence is demonstrated, thus allowing for polarimetric glucose measurements in samples with time-varying birefringence contributions. METHODS: In this paper, using a custom-designed laser-based optical polarimetry system with an integrated birefringence compensator, noninvasive glucose measurements in the physiological range are accurately measured within various birefringent samples similar in structure to the eye. RESULTS: Using the laser-based polarimetric approach, it is shown that glucose levels within the physiological range in the presence of significant varying birefringence can be accurately predicted with as low as 13.84 mg/dL error. CONCLUSIONS: The ability to compensate for corneal birefringence effects provides promise for the eventual development of a commercial home-based noninvasive polarimetric glucose monitor.