Although the scientific literature contains numerous reports of the statistical accuracy of systems for self-monitoring of blood glucose (SMBG), most of these studies determine accuracy in ways that may not be clinically useful. We have developed an error grid analysis (EGA), which describes the clinical accuracy of SMBG systems over the entire range of blood glucose values, taking into account 1) the absolute value of the system-generated glucose value, 2) the absolute value of the reference blood glucose value, 3) the relative difference between these two values, and 4) the clinical significance of this difference. The EGA of accuracy of five different reflectance meters (Eyetone, Dextrometer, Glucometer I, Glucometer II, Memory Glucometer II), a visually interpretable glucose reagent strip (Glucostix), and filter-paper spot glucose determinations is presented. In addition, reanalyses of a laboratory comparison of three reflectance meters (Accucheck II, Glucometer II, Glucoscan 9000) and of two previously published studies comparing the accuracy of five different reflectance meters with EGA is described. EGA provides the practitioner and the researcher with a clinically meaningful method for evaluating the accuracy of blood glucose values generated with various monitoring systems and for analyzing the clinical implications of previously published data.

Background-The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published.

Rapid, site-specific labeling of proteins with diverse probes remains an outstanding challenge for chemical biologists. Enzyme-mediated labeling approaches may be rapid but use protein or peptide fusions that introduce perturbations into the protein under study and may limit the sites that can be labeled, while many “bioorthogonal” reactions for which a component can be genetically encoded are too slow to effect quantitative site-specific labeling of proteins on a time scale that is useful for studying many biological processes. We report a fluorogenic reaction between bicyclo[6.1.0]non-4-yn-9-ylmethanol (BCN) and tetrazines that is 3–7 orders of magnitude faster than many bioorthogonal reactions. Unlike the reactions of strained alkenes, including trans-cyclooctenes and norbornenes, with tetrazines, the BCN–tetrazine reaction gives a single product of defined stereochemistry. We have discovered aminoacyl-tRNA synthetase/tRNA pairs for the efficient site-specific incorporation of a BCN-containing amino acid, 1, and a trans-cyclooctene-containing amino acid 2 (which also reacts extremely rapidly with tetrazines) into proteins expressed in Escherichia coli and mammalian cells. We demonstrate the rapid fluorogenic labeling of proteins containing 1 and 2 in vitro, in E. coli, and in live mammalian cells. These approaches may be extended to site-specific protein labeling in animals, and we anticipate that they will have a broad impact on labeling and imaging studies.

IDDM subjects who believe they have reduced awareness of hypoglycemia are generally correct. They have a history of more moderate and severe hypoglycemia, are less accurate at detecting BG < 3.9 mmol/l, and prospectively experience more moderate and severe hypoglycemia than do aware subjects. Neither disease duration nor level of glucose control explains their reduced awareness of hypoglycemia. Reduced-awareness individuals may benefit from interventions designed to teach them to recognize all of their potential early warning symptoms.

Hypoglycemia can lead to various aversive symptomatic, affective, cognitive, physiological, and social consequences, which in turn can lead to the development of possible phobic avoidance behaviors associated with hypoglycemia. On the other hand, some patients may inappropriately deny or disregard warning signs of hypoglycemia. This study presents preliminary reliability and validity data on a psychometric instrument designed to quantify this fear: the hypoglycemic fear survey. The instrument was found to have internal consistency and test-retest stability, to covary with elevated glycosylated hemoglobin, and to be sensitive to a behavioral treatment program designed to increase awareness of hypoglycemia.

OBJECTIVE -Recent studies show the importance of controlling blood glucose variability in relationship to both reducing hypoglycemia and attenuating the risk for cardiovascular and behavioral complications due to hyperglycemia. It is therefore important to design variability measures that are equally predictive of low and high blood glucose excursions.RESEARCH DESIGN AND METHODS -We introduce the average daily risk range (ADRR), a variability measure computed from routine self-monitored blood glucose (SMBG) data. The ADRR was constructed using a development dataset for 39 and 31 adults with type 1 and type 2 diabetes, respectively. The formula was then fixed, and the ADRR was compared against other variability measures using an independent validation dataset containing ϳ4 months of SMBG for 254 and 81 adults with type 1 and type 2 diabetes.RESULTS -From the 1st month of validation SMBG data, we computed the ADRR, blood glucose SD and coefficient of variation, daily blood glucose range and interquartile range, mean amplitude of glycemic excursion, M-value, and lability index. Then all measures were tested as predictors of low blood glucose (Ͻ2.2 mmol/l; Ͻ3.9 mmol/l) and high (Ͼ10 mmol/l; Ͼ22.2 mmol/l) events in the subsequent 3 months. The ADRR was the best predictor of both hypoglycemia and hyperglycemia, with a 6-fold increase in the likelihood of hypoglycemia and 3.5-fold increase in the likelihood of hyperglycemia across its risk categories.CONCLUSIONS -In a large SMBG database, the ADRR showed strong association with subsequent out-of-control glucose readings. Compared with other variability measures, the ADRR demonstrated a superior balance of sensitivity to predicting both hypoglycemia and hyperglycemia. This prediction was independent from type of diabetes. Diabetes Care 29:2433-2438, 2006H bA 1c (A1C) is the standard measure of average glycemic control (1) predicting diabetes complications in type 1 and type 2 diabetes (2,3). However, in addition to establishing A1C, the Diabetes Control and Complications Trial concluded that "A1C is not the most complete expression of the degree of glycemia. Other features of diabetic glucose control, which are not reflected by A1C, may add to or modify the risk of complications. For example, the risk of complications may be more highly dependent on the extent of postprandial glycemic excursions" (4). Consequently, contemporary studies increasingly investigate blood glucose fluctuations, specifically hypoglycemia and postprandial glycemic elevation.Hypoglycemia is common in type 1 diabetes (5) and becomes more prevalent in type 2 diabetes with treatment intensification (6). State-of-the-art therapies are still imperfect and may trigger acute blood glucose lowering, potentially leading to cognitive dysfunction, stupor, or death. Consequently, hypoglycemia has been identified as the primary barrier to optimal diabetes management (7,8). However, A1C is a poor predictor of hypoglycemic episodes, accounting for ϳ8% of future severe hypoglycemia (5). In contrast, specific var...

The provision of health care over the Internet is a rapidly evolving and potentially beneficial means of delivering treatment otherwise unsought or unobtainable. Internet interventions are typically behavioral treatments operationalized and transformed for Web delivery with the goal of symptom improvement. The literature on the feasibility and utility of Internet interventions is limited, and there are even fewer outcome study findings. This article reviews empirically tested Internet interventions and provides an overview of the issues in developing and/or using them in clinical practice. Future directions and implications are also addressed. Although Internet interventions will not likely replace face-to-face care, there is little doubt that they will grow in importance as a powerful component of successful psychobehavioral treatment.

The ability to introduce different biophysical probes into defined positions in target proteins will provide powerful approaches for interrogating protein structure, function and dynamics. However, methods for site-specifically incorporating multiple distinct unnatural amino acids are hampered by their low efficiency. Here we provide a general solution to this challenge by developing an optimized orthogonal translation system that uses amber and evolved quadruplet-decoding transfer RNAs to encode numerous pairs of distinct unnatural amino acids into a single protein expressed in Escherichia coli with a substantial increase in efficiency over previous methods. We also provide a general strategy for labelling pairs of encoded unnatural amino acids with different probes via rapid and spontaneous reactions under physiological conditions. We demonstrate the utility of our approach by genetically directing the labelling of several pairs of sites in calmodulin with fluorophores and probing protein structure and dynamics by Förster resonance energy transfer.