Sympatholytic Mechanisms for the Beneficial Cardiovascular Effects of SGLT2 Inhibitors: A Research Hypothesis for Dapagliflozin’s Effects in the Adrenal Gland

Heart failure (HF) remains the leading cause of morbidity and death in the western world, and new therapeutic modalities are urgently needed to improve the lifespan and quality of life of HF patients. The sodium-glucose co-transporter-2 (SGLT2) inhibitors, originally developed and mainly indicated for diabetes mellitus treatment, have been increasingly shown to ameliorate heart disease, and specifically HF, in humans, regardless of diabetes co-existence. Indeed, dapagliflozin has been reported to reduce cardiovascular mortality and hospitalizations in patients with HF and reduced ejection fraction (HFrEF). This SGLT2 inhibitor demonstrates these benefits also in non-diabetic subjects, indicating that dapagliflozin’s efficacy in HF is independent of blood glucose control. Evidence for the effectiveness of various SGLT2 inhibitors in providing cardiovascular benefits irrespective of their effects on blood glucose regulation have spurred the use of these agents in HFrEF treatment and resulted in FDA approvals for cardiovascular indications. The obvious question arising from all these studies is, of course, which molecular/pharmacological mechanisms underlie these cardiovascular benefits of the drugs in diabetics and non-diabetics alike. The fact that SGLT2 is not significantly expressed in cardiac myocytes (SGLT1 appears to be the dominant isoform) adds even greater perplexity to this answer. A variety of mechanisms have been proposed over the past few years and tested in cell and animal models and prominent among those is the potential for sympatholysis, i.e., reduction in sympathetic nervous system activity. The latter is known to be high in HF patients, contributing significantly to the morbidity and mortality of the disease. The present minireview first summarizes the current evidence in the literature supporting the notion that SGLT2 inhibitors, such as dapagliflozin and empagliflozin, exert sympatholysis, and also outlines the main putative underlying mechanisms for these sympatholytic effects. Then, we propose a novel hypothesis, centered on the adrenal medulla, for the sympatholytic effects specifically of dapagliflozin. Adrenal medulla is responsible for the production and secretion of almost the entire amount of circulating epinephrine and of a significant percentage of circulating norepinephrine in the human body. If proven true experimentally, this hypothesis, along with other emerging experimental evidence for sympatholytic effects in neurons, will shed new light on the pharmacological effects that mediate the cardiovascular benefits of SGLT2 inhibitor drugs, independently of their blood glucose-lowering effects.     

Abnormality segmentation in brain images via distributed estimation

The aim of this paper is to introduce a novel semisupervised scheme for abnormality detection and segmentation in medical images. Semisupervised learning does not require pathology modeling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. The likelihood function consists of a model and a data term and is formulated as a quadratic programming problem. The method is applied for automatically segmenting brain pathologies, such as simulated brain infarction and dysplasia, as well as real lesions in diabetes patients. The assessment of the method using receiver operating characteristic analysis demonstrates improvement in image segmentation over two-group analysis performed with Statistical Parametric Mapping (SPM).     

Robust white matter lesion segmentation in FLAIR MRI

This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.     

Environmental impact of the use of contaminated sediments as partial replacement of the aggregate used in road construction

The Indiana Harbor Canal (IHC) is a waterway extensively polluted with heavy metals and petroleum. Since there are limited disposal options for the petroleum-contaminated sediments (PCSs) of the canal, the environmental impact of IHC dewatered sediment when used as partial replacement of the aggregate used in hot mix asphalt (HMA) for road construction was investigated. In order to assess the long term migration of the target contaminants into the environment, the TCLP, SPLP, and a Constant pH leaching test were applied to a HMA mixture containing 10% of dewatered PCS, a conventional HMA, and the dewatered PCS. None of the heavy metals significantly leached from any of the tested materials in any of the conducted tests. Despite the presence of PAHs in the PCS, these were not found in any of the leachate samples. Finally, among the measured VOCs, only acetone and 2-butanone were found to leach from the asphalt mixtures and the sediment in the Constant pH experiment. It was concluded that it may be environmentally safe to replace the aggregates of the HMA used in road construction in the studied proportions with dewatered PCS based upon leaching levels as compared to TCLP regulated levels. This could be a viable, beneficial use option for the PCS, and therefore, for the canal remediation.     

An activity-based costing model for long-term preservation and dissemination of digital research data: the case of DANS

Financial sustainability is an important attribute of a trusted, reliable digital repository. The authors of this paper use the case study approach to develop an activity-based costing (ABC) model. This is used for estimating the costs of preserving digital research data and identifying options for improving and sustaining relevant activities. The model is designed in the environment of the Data Archiving and Networked Services (DANS) institute, a well-known trusted repository. The DANS–ABC model has been tested on empirical cost data from activities performed by 51 employees in frames of over 40 different national and international projects. Costs of resources are being assigned to cost objects through activities and cost drivers. The ‘euros per dataset’ unit of costs measurement is introduced to analyse the outputs of the model. Funders, managers and other decision-making stakeholders are being provided with understandable information connected to the strategic goals of the organisation. The latter is being achieved by linking the DANS–ABC model to another widely used managerial tool—the Balanced Scorecard (BSC). The DANS–ABC model supports costing of services provided by a data archive, while the combination of the DANS–ABC with a BSC identifies areas in the digital preservation process where efficiency improvements are possible.     

Optimal newsvendor policies for dual-sourcing supply chains: A disruption risk management framework

We propose generic single period (newsvendor-type) inventory models for capturing the trade-off between inventory policies and disruption risks in a dual-sourcing supply chain network both unconstrained and under service level constraints, where both supply channels are susceptible to disruption risks. The models are developed for both risk neutral and risk-averse decision-makers and can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Analytical closed-form solutions are obtained and interesting managerial insights on the merit of contingency strategies in managing uncertainties and risks in dual-sourcing supply chains are discussed.     

A total fatigue life model for the prediction of the R-ratio effects on fatigue crack growth of adhesively-bonded pultruded GFRP DCB joints

A new phenomenological fatigue crack growth formulation for the modeling and the prediction of the Mode I fatigue behavior of adhesively-bonded pultruded glass fiber-reinforced polymer double cantilever beam joints under different R-ratios is introduced. The established formulation is based on the total fatigue life concept, considering however the model parameters as functions of the R-ratio by fitting the existing experimental data under two to three different R-ratios. This model can subsequently be used for the derivation of fatigue crack growth curves under any different R-ratio, thus assisting the development of methodologies for the fatigue life prediction of a joint comprising adherends with the same material under realistic loading conditions. An extensive fatigue/fracture database has been derived, containing results of 28 Mode I fatigue experiments, to assist the model development and to validate its predictions. Comparison of the model predictions and experimental results proved the model’s validity.     

Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation

Individual mouse embryonic stem cells have been found to exhibit highly variable differentiation responses under the same environmental conditions. The noisy cyclic expression of Hes1 and its downstream genes are known to be responsible for this, but the mechanism underlying this variability in expression is not well understood. In this paper, we show that the observed experimental data and diverse differentiation responses can be explained by a spatial stochastic model of the Hes1 gene regulatory network. We also propose experiments to control the precise differentiation response using drug treatment.