XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Photoacoustic (PA) imaging, also called optoacoustic imaging, is a new biomedical imaging modality based on the use of laser-generated ultrasound that has emerged over the last decade. It is a hybrid modality, combining the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging. In essence, a PA image can be regarded as an ultrasound image in which the contrast depends not on the mechanical and elastic properties of the tissue, but its optical properties, specifically optical absorption. As a consequence, it offers greater specificity than conventional ultrasound imaging with the ability to detect haemoglobin, lipids, water and other light-absorbing chomophores, but with greater penetration depth than purely optical imaging modalities that rely on ballistic photons. As well as visualizing anatomical structures such as the microvasculature, it can also provide functional information in the form of blood oxygenation, blood flow and temperature. All of this can be achieved over a wide range of length scales from micrometres to centimetres with scalable spatial resolution. These attributes lend PA imaging to a wide variety of applications in clinical medicine, preclinical research and basic biology for studying cancer, cardiovascular disease, abnormalities of the microcirculation and other conditions. With the emergence of a variety of truly compelling in vivo images obtained by a number of groups around the world in the last 2–3 years, the technique has come of age and the promise of PA imaging is now beginning to be realized. Recent highlights include the demonstration of whole-body small-animal imaging, the first demonstrations of molecular imaging, the introduction of new microscopy modes and the first steps towards clinical breast imaging being taken as well as a myriad of in vivo preclinical imaging studies. In this article, the underlying physical principles of the technique, its practical implementation, and a range of clinical and preclinical applications are reviewed.

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Biomedical photoacoustic imaging

Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. We present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera.

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Contrast restoration of weather degraded images

Imaging polarimetry has emerged over the past three decades as a powerful tool to enhance the information available in a variety of remote sensing applications. We discuss the foundations of passive imaging polarimetry, the phenomenological reasons for designing a polarimetric sensor, and the primary architectures that have been exploited for developing imaging polarimeters. Considerations on imaging polarimeters such as calibration, optimization, and error performance are also discussed. We review many important sources and examples from the scientific literature.

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Review of passive imaging polarimetry for remote sensing applications

This work presents the first step toward a glucose biosensor using surface-enhanced Raman spectroscopy (SERS). Historically, glucose has been extremely difficult to detect by SERS because it has a small normal Raman cross section and adsorbs weakly or not at all to bare silver surfaces. In this paper, we report the first systematic study of the direct detection of glucose using SERS. Glucose is partitioned into an alkanethiol monolayer adsorbed on a silver film over nanosphere (AgFON) surface and thereby, it is preconcentrated within the 0-4 nm thick zone of electromagnetic field enhancement. The experiments presented herein utilize leave-one-out partial least-squares (LOO-PLS) analysis to demonstrate quantitative glucose detection both over a large (0-250 mM) and clinically relevant (0-25 mM) concentration range. The root-mean-squared error of prediction (RMSEP) of 1.8 mM (33.1 mg/dL) in the clinical study is near that desired for medical applications (1 mM, 18 mg/dL). Future studies will advance toward true in vivo, real time, minimally invasive sensing.

Toward a Glucose Biosensor Based on Surface-Enhanced Raman Scattering

In our recent Letter,(1) several typographical errors were present. On p. 487, in Fig. 2, the equations for the following Mueller matrix elements should read as S(14) = (RO - LO), S(22) = (HH + VV) - (HV + VH), S(23) = (PH + MV) - (PV + MH), S(24) = (RH + LV) - (RV + LH), S(32) = (HP + VM) - (HM + VP), S(33) = (PP + MM) - (PM + MP), S(34) = (RP + LM) - (RM + LP), S(41) = (OR + OL), S(42) = (HR + VL) - (HL + VR), S(43) = (PR + ML) - (PL + MR), and S(44) = (RR + LL) - (RL + LR). Also on p. 487, in the left-hand column, line 10 from the top should read as follows: mfp? = 1/[mua + mus(1 - g)], was 0.957 cm.

/pdf/measurement-and-calculation-of-the-two-dimensional-2p5aqbqayz.pdf

Measurement and calculation of the two-dimensional backscattering Mueller matrix of a turbid medium.

We present both experimental measurements and Monte-Carlo-based simulations of the diffusely backscattered intensity patterns that arise from illuminating a turbid medium with a polarized laser beam. It is rigorously shown that, because of axial symmetry of the system, only seven elements of the effective backscattering Mueller matrix are independent. A new numerical method that allows simultaneous calculation of all 16 elements of the two-dimensional Mueller matrix is used. To validate our method we compared calculations to measurements from a turbid medium that consisted of polystyrene spheres of different sizes and concentrations in deionized water. The experimental and numerical results are in excellent agreement.

/pdf/light-backscattering-polarization-patterns-from-turbid-media-2j597836pi.pdf

Light backscattering polarization patterns from turbid media: theory and experiment.

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 ...

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

The high fatality rate associated with the late detection of skin cancer makes early detection crucial in preventing death. The current method for determining if a skin lesion is suspect to cancer is initially based on the patient's and physician's subjective observation of the skin lesion. Physicians use a set of parameters called the ABCD (asymmetry, border, color, diameter) rule to help facilitate diagnosis of potential cancerous lesions. Lesions that are suspicious then require a biopsy, which is a painful, invasive, and a time-consuming procedure. In an attempt to reduce the aforementioned undesirable elements cur- rently associated with skin cancer diagnosis, a novel optical polarization-imaging system is described that has the potential to non- invasively detect cancerous lesions. The described system generates the full 16-element Mueller matrix in less than 70 s. The operation of the system was tested in transmission, specular reflection, and diffuse reflectance modes, using known samples, such as a horizontal linear polarizer, a mirror, and a diffuser plate. In addition, it was also used to image a benign lesion on a human subject. The results of the known samples are in good agreement with their theoretical values with an average accuracy of 97.96% and a standard deviation of 0.0084, us- ing 16 polarization images. The system accuracy was further in- creased to 99.44% with a standard deviation of 0.005, when 36 im- ages were used to generate the Mueller matrix. © 2002 Society of Photo- Optical Instrumentation Engineers. (DOI: 10.1117/1.1486248)

Development and calibration of an automated Mueller matrix polarization imaging system

A polarimetric glucose sensor utilizing a digital closed-loop controller was designed and implemented during this study. Its potential as a noninvasive glucose sensor was evaluated in vitro for both glucose doped water and bovine aqueous humor mediums. A physiological hyperglycemic concentration range was used in both calibration and validation of each set of experiments. Ideally, the end application of this system could estimate blood glucose concentrations indirectly by measuring the amount of rotation of a light beam's polarization state after it propagates through the aqueous humor contained within the anterior chamber of the eye. The polarimeter designed in this study differs from similar investigated systems in that it utilizes a digital closed-loop control system. This type of controller was implemented in order to further improve system repeatability and stability without sacrificing accuracy. Unique to this investigation, independent validation sets other than those used to create each respective calibration model were obtained. The results of the glucose-doped water experiments yielded mean standard errors of prediction for calibration and validation of 6.91 and 8.84 mg/dl, respectively. The mean standard errors of prediction during calibration and validation of the glucose-doped aqueous humor experiments were higher at 27.20 and 27.47 mg/dl, respectively, due to medium degradation over time while exposed to air.

Brent D. Cameron

Papers

Measurement and calculation of the two-dimensional backscattering Mueller matrix of a turbid medium.

Light backscattering polarization patterns from turbid media: theory and experiment.

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

Development and calibration of an automated Mueller matrix polarization imaging system

Noninvasive glucose sensing utilizing a digital closed-loop polarimetric approach