What is the mechanism of microalbuminuria in diabetes: a role for the glomerular endothelium?

Microalbuminuria is an important risk factor for cardiovascular disease and progressive renal impairment. This holds true in the general population and particularly in those with diabetes, in whom it is common and marks out those likely to develop macrovascular disease and progressive renal impairment. Understanding the pathophysiological mechanisms through which microalbuminuria occurs holds the key to designing therapies to arrest its development and prevent these later manifestations

Skin microvascular vasodilatory capacity in offspring of two parents with Type 2 diabetes

Aims<br/> Microvascular dysfunction occurs in Type 2 diabetes and in subjects with fasting hyperglycaemia. It is unclear whether this dysfunction relates to dysglycaemia. This study investigated in normogylcaemic individuals whether a genetic predisposition to diabetes, or indices of insulin resistance including endothelial markers, were associated with impaired microvascular function.<br/> Methods<br/> Maximum microvascular hyperaemia to local heating of the skin was measured using laser Doppler flowmetry in 21 normoglycaemic subjects with no family history of diabetes (Group 1) and 21 normoglycaemic age, sex and body mass index-matched offspring of two parents with Type 2 diabetes (Group 2). <br/>Results<br/> Although Group 2 had normal fasting plasma glucose and glucose tolerance tests, the 120-min glucose values were significantly higher at 6.4 (5.3-6.6) mmol/l (median (25th-75th centile)) than the control group at 4.9 (4.6-5.9) mmol/l (P=0.005) and the insulinogenic index was lower at 97.1 (60.9-130.8) vs. 124.0 (97.2-177.7) (P=0.027). Skin maximum microvascular hyperaemia (Group 1: 1.56 (1.39- 1.80) vs. Group 2: 1.53 (1.30-1.98) V, P=0.99) and minimum microvascular resistance which normalizes the hyperaemia data for blood pressure (Group 1: 52.0 (43.2-67.4) vs. Group 2: 56.0 (43.7-69.6) mmHgN, P=0.70) did not differ in the two groups. Significant positive associations occurred between minimum microvascular resistance and indices of the insulin resistance syndrome; plasminogen activator inhibitor type 1 (R-s=0.46, P=0.003), t-PA (R-s=0.36, P=0.03), total cholesterol (R-s=0.35, P=0.02), and triglyceride concentration (R-s=0.35, P=0.02), and an inverse association with insulin sensitivity (R-s=-0.33, P=0.03).<br/> Conclusions<br/> In normoglycaemic adults cutaneous microvascular vasodilatory capacity is associated with features of insulin resistance syndrome, particularly with plasminogen activator inhibitor type 1. A strong family history of Type 2 diabetes alone does not result in impairment in the maximum hyperaemic response

Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

Understanding allostery in enzymes and tools to identify it, offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences

Mechanistic Insights into β-Lactamase-Catalysed Carbapenem Degradation Through Product Characterisation

β-Lactamases are a major threat to the clinical use of carbapenems, which are often antibiotics of last resort. Despite this, the reaction outcomes and mechanisms by which β-lactamases degrade carbapenems are still not fully understood. The carbapenem bicyclic core consists of a β-lactam ring fused to a pyrroline ring. Following β-lactamase-mediated opening of the β-lactam, the pyrroline may interconvert between an enamine (2-pyrroline) form and two epimeric imine (1-pyrroline) forms; previous crystallographic and spectroscopic studies have reported all three of these forms in the contexts of hydrolysis by different β-lactamases. As we show by NMR spectroscopy, the serine β-lactamases (KPC-2, SFC-1, CMY-10, OXA-23, and OXA-48) and metallo-β-lactamases (NDM-1, VIM-1, BcII, CphA, and L1) tested all degrade carbapenems to preferentially give the Δ2 (enamine) and/or (R)-Δ1 (imine) products. Rapid non-enzymatic tautomerisation of the Δ2 product to the (R)-Δ1 product prevents assignment of the nascent enzymatic product by NMR. The observed stereoselectivity implies that carbapenemases control the form of their pyrroline ring intermediate(s)/product(s), thereby preventing pyrroline tautomerisation from inhibiting catalysis.FWN – Publicaties zonder aanstelling Universiteit Leide

A New Mechanism for β‐Lactamases: Class D Enzymes Degrade 1β‐Methyl Carbapenems through Lactone Formation

β‐Lactamases threaten the clinical use of carbapenems, which are considered antibiotics of last resort. The classical mechanism of serine carbapenemase catalysis proceeds through hydrolysis of an acyl‐enzyme intermediate. We show that class D β‐lactamases also degrade clinically used 1β‐methyl‐substituted carbapenems through the unprecedented formation of a carbapenem‐derived β‐lactone. β‐Lactone formation results from nucleophilic attack of the carbapenem hydroxyethyl side chain on the ester carbonyl of the acyl‐enzyme intermediate. The carbapenem‐derived lactone products inhibit both serine β‐lactamases (particularly class D) and metallo‐β‐lactamases. These results define a new mechanism for the class D carbapenemases, in which a hydrolytic water molecule is not required.FWN – Publicaties zonder aanstelling Universiteit Leide

A New Mechanism for β‐Lactamases: Class D Enzymes Degrade 1β‐Methyl Carbapenems through Lactone Formation

β‐Lactamases threaten the clinical use of carbapenems, which are considered antibiotics of last resort. The classical mechanism of serine carbapenemase catalysis proceeds through hydrolysis of an acyl‐enzyme intermediate. We show that class D β‐lactamases also degrade clinically used 1β‐methyl‐substituted carbapenems through the unprecedented formation of a carbapenem‐derived β‐lactone. β‐Lactone formation results from nucleophilic attack of the carbapenem hydroxyethyl side chain on the ester carbonyl of the acyl‐enzyme intermediate. The carbapenem‐derived lactone products inhibit both serine β‐lactamases (particularly class D) and metallo‐β‐lactamases. These results define a new mechanism for the class D carbapenemases, in which a hydrolytic water molecule is not required.FWN – Publicaties zonder aanstelling Universiteit Leide

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations

Graduates of different UK medical schools show substantial differences in performance on MRCP(UK) Part 1, Part 2 and PACES examinations

Background: The UK General Medical Council has emphasized the lack of evidence on whether graduates from different UK medical schools perform differently in their clinical careers. Here we assess the performance of UK graduates who have taken MRCP( UK) Part 1 and Part 2, which are multiple-choice assessments, and PACES, an assessment using real and simulated patients of clinical examination skills and communication skills, and we explore the reasons for the differences between medical schools. Method: We perform a retrospective analysis of the performance of 5827 doctors graduating in UK medical schools taking the Part 1, Part 2 or PACES for the first time between 2003/2 and 2005/3, and 22453 candidates taking Part 1 from 1989/1 to 2005/3. Results: Graduates of UK medical schools performed differently in the MRCP( UK) examination between 2003/2 and 2005/3. Part 1 and 2 performance of Oxford, Cambridge and Newcastle-upon-Tyne graduates was significantly better than average, and the performance of Liverpool, Dundee, Belfast and Aberdeen graduates was significantly worse than average. In the PACES ( clinical) examination, Oxford graduates performed significantly above average, and Dundee, Liverpool and London graduates significantly below average. About 60% of medical school variance was explained by differences in pre-admission qualifications, although the remaining variance was still significant, with graduates from Leicester, Oxford, Birmingham, Newcastle-upon-Tyne and London overperforming at Part 1, and graduates from Southampton, Dundee, Aberdeen, Liverpool and Belfast underperforming relative to pre-admission qualifications. The ranking of schools at Part 1 in 2003/2 to 2005/3 correlated 0.723, 0.654, 0.618 and 0.493 with performance in 1999-2001, 1996-1998, 1993-1995 and 1989-1992, respectively. Conclusion: Candidates from different UK medical schools perform differently in all three parts of the MRCP( UK) examination, with the ordering consistent across the parts of the exam and with the differences in Part 1 performance being consistent from 1989 to 2005. Although pre-admission qualifications explained some of the medical school variance, the remaining differences do not seem to result from career preference or other selection biases, and are presumed to result from unmeasured differences in ability at entry to the medical school or to differences between medical schools in teaching focus, content and approaches. Exploration of causal mechanisms would be enhanced by results from a national medical qualifying examination