Recent trials of intensive glycemic control suggest a possible link between hypoglycemia and excess cardiovascular mortality in patients with type 2 diabetes. Hypoglycemia might cause arrhythmias through effects on cardiac repolarization and changes in cardiac autonomic activity. Our aim was to study the risk of arrhythmias during spontaneous hypoglycemia in type 2 diabetic patients with cardiovascular risk. Twenty-five insulin-treated patients with type 2 diabetes and a history of cardiovascular disease or two or more risk factors underwent simultaneous continuous interstitial glucose and ambulatory electrocardiogram monitoring. Frequency of arrhythmias, heart rate variability, and markers of cardiac repolarization were compared between hypoglycemia and euglycemia and between hyperglycemia and euglycemia matched for time of day. There were 134 h of recording at hypoglycemia, 65 h at hyperglycemia, and 1,258 h at euglycemia. Bradycardia and atrial and ventricular ectopic counts were significantly higher during nocturnal hypoglycemia compared with euglycemia. Arrhythmias were more frequent during nocturnal versus daytime hypoglycemia. Excessive compensatory vagal activation after the counterregulatory phase may account for bradycardia and associated arrhythmias. QT intervals, corrected for heart rate, >500 ms and abnormal T-wave morphology were observed during hypoglycemia in some participants. Hypoglycemia, frequently asymptomatic and prolonged, may increase the risk of arrhythmias in patients with type 2 diabetes and high cardiovascular risk. This is a plausible mechanism that could contribute to increased cardiovascular mortality during intensive glycemic therapy.

It has been hypothesized that protease-activated receptors may be activated and attenuated by more than one protease. Here, we explore a desensitization mechanism of the PAR1 thrombin receptor by anticoagulant proteases and provide an explanation to the enigma of why plasmin/tissue plasminogen activator (t-PA) can both activate and deactivate platelets prior to thrombin treatment. By using a soluble N-terminal exodomain (TR78) as a model for the full-length receptor, we were able to unambiguously compare cleavage rates and specificities among the serum proteases. Thrombin cleaves TR78 at the R41-S42 peptide bond with a kcat of 120 s-1 and a KM of 16 microM to produce TR62 (residues 42-103). We found that, of the anticoagulant proteases, only plasmin can rapidly truncate the soluble exodomain at the R70/K76/K82 sites located on a linker region that tethers the ligand to the body of the receptor. Plasmin cleavage of the TR78 exodomain is nearly equivalent to that of thrombin cleavage at R41 with similar rates (kcat = 30 s-1) and affinity (KM = 18 microM). Specificity was demonstrated since there is no observed cleavage at the five other potential plasmin-cleavage sites. Plasmin also cleaves the TR78 exodomain at the R41 thrombin-cleavage site generating transiently activated exodomain. We directly demonstrated that plasmin cleaves these same sites in full-length membrane-embedded receptor expressed in yeast and COS7 fibroblasts. The rate of plasmin truncation is similar between the extensively glycosylated COS7-expressed receptor and the nonglycosylated yeast-produced receptor. Mutation of the R70/K76/K82 sites to A70/A76/A82 eliminates plasmin truncation and desensitization of thrombin-dependent Ca2+ signaling and converts PAR1 into a plasmin-activated receptor with full agonist activity for plasmin. Plasmin does not desensitize the Ca2+ response of platelets or COS7 cells to SFLLRN consistent with intermolecular ligand-binding sites being located to the C-terminal side of K82. Truncation of the wild-type receptor at the C-terminal plasmin-cleavage sites removes the N-terminal tethered ligand or preligand, thereby providing an effective pathway for PAR1 desensitization in vivo.

Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the functional forms of either the preferential attachment function or fitness function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential attachment and node fitness without imposing such functional constraints that works by maximizing a log-likelihood function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.

OBJECTIVEHypoglycemia may exert proarrhythmogenic effects on the heart via sympathoadrenal stimulation and hypokalemia. Hypoglycemia-induced cardiac dysrhythmias are linked to the "dead-in-bed syndrome," a rare but devastating condition. We examined the effect of nocturnal and daytime clinical hypoglycemia on electrocardiogram (ECG) in young people with type 1 diabetes. RESEARCH DESIGN AND METHODSThirty-seven individuals with type 1 diabetes underwent 96 h of simultaneous ambulatory ECG and blinded continuous interstitial glucose monitoring (CGM) while symptomatic hypoglycemia was recorded. Frequency of arrhythmias, heart rate variability, and cardiac repolarization were measured during hypoglycemia and compared with time-matched euglycemia during night and day. RESULTSA total of 2,395 h of simultaneous ECG and CGM recordings were obtained; 159 h were designated hypoglycemia and 1,355 h euglycemia. A median duration of nocturnal hypoglycemia of 60 min (interquartile range 40-135) was longer than daytime hypoglycemia of 44 min (30-70) (P = 0.020). Only 24.1% of nocturnal and 51.0% of daytime episodes were symptomatic. Bradycardia was more frequent during nocturnal hypoglycemia compared with matched euglycemia (incident rate ratio [IRR] 6.44 [95% CI 6.26, 6.63], P < 0.001). During daytime hypoglycemia, bradycardia was less frequent (IRR 0.023 [95% CI 0.002, 0.26], P = 0.002) and atrial ectopics more frequent (IRR 2.29 [95% CI 1.19,4.39], P = 0.013). Prolonged QTc, T-peak to T-end interval duration, and decreased T-wave symmetry were detected during nocturnal and daytime hypoglycemia. CONCLUSIONSAsymptomatic hypoglycemia was common. We identified differences in arrhythmic risk and cardiac repolarization during nocturnal versus daytime hypoglycemia in young adults with type 1 diabetes. Our data provide further evidence that hypoglycemia is proarrhythmogenic.Hypoglycemia is an inevitable consequence of the current management of type 1 diabetes (1). Improved glycemic control is frequently accompanied by an increased risk of inducing iatrogenic hypoglycemia (2). Observational studies indicate that rates of severe hypoglycemia have generally not fallen despite the introduction of insulin

Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman’s original method which had evidently gone unnoticed since its publication over a decade ago.