A double-blind randomized study comparing the effects of continuing or not continuing rosiglitazone + metformin therapy when starting insulin therapy in people with type 2 diabetes

Aims: To compare the efficacy and safety of either continuing or discontinuing rosiglitazone + metformin fixed-dose combination when starting insulin therapy in people with Type 2 diabetes inadequately controlled on oral therapy. Methods: In this 24-week double-blind study, 324 individuals with Type 2 diabetes inadequately controlled on maximum dose rosiglitazone + metformin therapy were randomly assigned to twice-daily premix insulin therapy (target pre-breakfast and pre-evening meal glucose ≤ 6.5 mmol/l) in addition to either rosiglitazone + metformin (8/2000 mg) or placebo. Results: Insulin dose at week 24 was significantly lower with rosiglitazone + metformin (33.5 ± 1.5 U/day, mean ± se) compared with placebo [59.0 ± 3.0 U/day; model-adjusted difference -26.6 (95% CI -37.7, -15,5) U/day, P < 0.001]. Despite this, there was greater improvement in glycaemic control [HbA 1c rosiglitazone + metformin vs. placebo 6.8 ± 0.1 vs. 7.5 ± 0.1%; difference -0.7 (-0.8, -0.5)%, P < 0.001] and more individuals achieved glycaemic targets (HbA1c < 7.0% 70 vs. 34%, P < 0.001). The proportion of individuals reporting at least one hypoglycaemic event during the last 12 weeks of treatment was similar in the two groups (rosiglitazone + metformin vs. placebo 25 vs. 27%). People receiving rosiglitazone + metformin in addition to insulin reported greater treatment satisfaction than those receiving insulin alone. Both treatment regimens were well tolerated but more participants had oedema [12 (7%) vs. 4 (3%)] and there was more weight gain [3.7 vs. 2.6 kg; difference 1.1 (0.2, 2.1) kg, P = 0.02] with rosiglitazone + metformin. Conclusions: Addition of insulin to rosiglitazone + metformin enabled more people to reach glycaemic targets with less insulin, and was generally well tolerated. © 2007 The Authors.

Subject identification via ECG fiducial-based systems:influence of the type of QT interval correction

Electrocardiography (ECG) has been recently proposed as biometric trait for identification purposes. Intra-individual variations of ECG might affect identification performance. These variations are mainly due to Heart Rate Variability (HRV). In particular, HRV causes changes in the QT intervals along the ECG waveforms. This work is aimed at analysing the influence of seven QT interval correction methods (based on population models) on the performance of ECG-fiducial-based identification systems. In addition, we have also considered the influence of training set size, classifier, classifier ensemble as well as the number of consecutive heartbeats in a majority voting scheme. The ECG signals used in this study were collected from thirty-nine subjects within the Physionet open access database. Public domain software was used for fiducial points detection. Results suggested that QT correction is indeed required to improve the performance. However, there is no clear choice among the seven explored approaches for QT correction (identification rate between 0.97 and 0.99). MultiLayer Perceptron and Support Vector Machine seemed to have better generalization capabilities, in terms of classification performance, with respect to Decision Tree-based classifiers. No such strong influence of the training-set size and the number of consecutive heartbeats has been observed on the majority voting scheme.

On the use of the type I and II scheme for classifying ultrafast laser direct-write photonics

The use of the Type I and Type II scheme, first introduced and used by fiber Bragg grating researchers, has recently been adopted by the ultrafast laser direct-write photonics community to classify the physical geometry of waveguides written into glasses and crystals. This has created confusion between the fiber Bragg grating and direct-write photonics community. Here we propose a return to the original basis of the classification based on the characteristics of the material modification rather than the physical geometry of the waveguide.

Paradoxical decrease in HDL-cholesterol and apolipoprotein A1 with simvastatin and atorvastatin in a patient with type 2 diabetes

Statins are agents widely used to lower LDL-cholesterol (LDL-C) in primary and secondary prevention of coronary heart disease. The five statins available in the UK (simvastatin, pravastatin, fluvastatin, atorvastatin and rosuvastatin) differ in many of their pharmacologic properties. In addition to lowering LDL-C, statins also increase HDL-cholesterol (HDL-C) moderately. There have been rare reports of significant HDL-C decreases in patients commenced on fibrates and when thiazolidinediones are added to fibrates. This is known as a 'paradoxical HDL-C decrease' as both groups of agents usually increase HDL-C. This phenomenon has never been clearly documented following statin therapy. We now describe a patient with type 2 diabetes who showed this paradoxical fall in HDL-C (baseline HDL-C: 1.8 mmol/L; on simvastatin 40 mg HDL-C 0.6 mmol/L; on atorvastatin 20 mg HDL-C 0.9 mmol/L) with a similar decrease in apolipoprotein A1. No similar decrease was observed with pravastatin and rosuvastatin therapy. This phenomenon appeared to be associated with statin treatment and not a statin/fibrate combination. Our patient clearly demonstrated a paradoxical HDL-C fall with simvastatin and atorvastatin, but not pravastatin or rosuvastatin. Simvastatin and atorvastatin share many pharmacokinetic properties such as lipophilicity while pravastatin and rosuvastatin are relatively hydrophilic and are not metabolized by cytochrome P450 3A4. However, these characteristics do not explain the dramatic reductions in HDL-C observed.

Concurrent codes:a holographic-type encoding robust against noise and loss

Concurrent coding is an encoding scheme with 'holographic' type properties that are shown here to be robust against a significant amount of noise and signal loss. This single encoding scheme is able to correct for random errors and burst errors simultaneously, but does not rely on cyclic codes. A simple and practical scheme has been tested that displays perfect decoding when the signal to noise ratio is of order -18dB. The same scheme also displays perfect reconstruction when a contiguous block of 40% of the transmission is missing. In addition this scheme is 50% more efficient in terms of transmitted power requirements than equivalent cyclic codes. A simple model is presented that describes the process of decoding and can determine the computational load that would be expected, as well as describing the critical levels of noise and missing data at which false messages begin to be generated.

Pharmacology and therapeutic implications of current drugs for type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a global epidemic that poses a major challenge to health-care systems. Improving metabolic control to approach normal glycaemia (where practical) greatly benefits long-term prognoses and justifies early, effective, sustained and safety-conscious intervention. Improvements in the understanding of the complex pathogenesis of T2DM have underpinned the development of glucose-lowering therapies with complementary mechanisms of action, which have expanded treatment options and facilitated individualized management strategies. Over the past decade, several new classes of glucose-lowering agents have been licensed, including glucagon-like peptide 1 receptor (GLP-1R) agonists, dipeptidyl peptidase 4 (DPP-4) inhibitors and sodium/glucose cotransporter 2 (SGLT2) inhibitors. These agents can be used individually or in combination with well-established treatments such as biguanides, sulfonylureas and thiazolidinediones. Although novel agents have potential advantages including low risk of hypoglycaemia and help with weight control, long-term safety has yet to be established. In this Review, we assess the pharmacokinetics, pharmacodynamics and safety profiles, including cardiovascular safety, of currently available therapies for management of hyperglycaemia in patients with T2DM within the context of disease pathogenesis and natural history. In addition, we briefly describe treatment algorithms for patients with T2DM and lessons from present therapies to inform the development of future therapies.