Coronavirus disease 2019 (COVID‐19), the worst pandemic in more than a century, has claimed >125,000 lives worldwide to date. Emerging predictors for poor outcomes include advanced age, male sex, ...preexisting cardiovascular disease, and risk factors including hypertension, diabetes, and, more recently, obesity. This article posits new obesity‐driven predictors of poor COVID‐19 outcomes, over and above the more obvious extant risks associated with obesity, including cardiometabolic disease and hypoventilation syndrome in intensive care patients. This article also outlines a theoretical mechanistic framework whereby adipose tissue in individuals with obesity may act as a reservoir for more extensive viral spread, with increased shedding, immune activation, and cytokine amplification. This paper proposes studies to test this reservoir concept with a focus on specific cytokine pathways that might be amplified in individuals with obesity and COVID‐19. Finally, this paper underscores emerging therapeutic strategies that might benefit subsets of patients in which cytokine amplification is excessive and potentially fatal.
Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective ...artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different accuracies depending on the glycemic subrange under consideration. Therefore technique selection depends on the type of alert required. Specific findings are that a linear Support Vector Regression-based model, trained with normal as well as polynomial features, is best for blood glucose level forecasting in the normal and hyperglycemic ranges while a Multilayer Perceptron trained on oversampled data is ideal for predictions in the hypoglycemic range.
Gut microbiota, obesity and diabetes Patterson, Elaine; Ryan, Paul M; Cryan, John F ...
Postgraduate medical journal,
05/2016, Volume:
92, Issue:
1087
Journal Article
Peer reviewed
Open access
The central role of the intestinal microbiota in the progression and, equally, prevention of metabolic dysfunction is becoming abundantly apparent. The symbiotic relationship between intestinal ...microbiota and host ensures appropriate development of the metabolic system in humans. However, disturbances in composition and, in turn, functionality of the intestinal microbiota can disrupt gut barrier function, a trip switch for metabolic endotoxemia. This low-grade chronic inflammation, brought about by the influx of inflammatory bacterial fragments into circulation through a malfunctioning gut barrier, has considerable knock-on effects for host adiposity and insulin resistance. Conversely, recent evidence suggests that there are certain bacterial species that may interact with host metabolism through metabolite-mediated stimulation of enteric hormones and other systems outside of the gastrointestinal tract, such as the endocannabinoid system. When the abundance of these keystone species begins to decline, we see a collapse of the symbiosis, reflected in a deterioration of host metabolic health. This review will investigate the intricate axis between the microbiota and host metabolism, while also addressing the promising and novel field of probiotics as metabolic therapies.
•3D random pore model for graphite oxidation includes closed pores, sample size, and shape.•Model agrees well with 30 experimental datasets covering 10 different graphite grades.•Oxidation is faster ...for finer pores, smaller samples, lower density, and/or higher reactivity.•Modeled reactivity ratios demonstrate reasonable activation energy for kinetic regime.•Current kinetic control formulation can be extended to include diffusion control.
Thermal oxidation mass loss of synthetic graphite can be life-limiting for high-temperature applications. The complex microstructure of synthetic graphite has made it difficult to predict oxidation-induced changes.Specifically, there are no accepted models for predicting oxidation pore growth, which affects properties and performance.
This paper presents the application of a three-dimensional microstructure-based model for synthetic graphite oxidation in the kinetic regime. Pore structure is described with randomly-placed spheres, whose growth rate is dependent on the penetration depth of oxidant. Pore structure, intrinsic reactivity, sample size, geometry, and initially closed porosity are included in the model. Oxidation mass loss occurs faster for finer pore size, lower bulk density, higher reactivity, and/or smaller samples.
For model validation, 30 datasets representing ten (mostly nuclear) high-purity graphite grades were selected, covering air oxidation temperatures from 450 to 750 °C and sample masses spanning four orders of magnitude. Agreements between model and experiment were judged reasonable, particularly the effects of microstructure and sample mass. For the temperature effect, aggregated reactivity ratios showed an activation energy of 187 kJ/mol, within expected kinetic range. To the author's knowledge, this model is the first three-dimensional microstructure-based approach for graphite air oxidation in the kinetic regime.
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On-Surface Synthesis of Nonmetal Porphyrins Baklanov, Aleksandr; Garnica, Manuela; Robert, Anton ...
Journal of the American Chemical Society,
01/2020, Volume:
142, Issue:
4
Journal Article
Peer reviewed
We report the on-surface synthesis of a nonmetal porphyrin, namely, silicon tetraphenylporphyrin (Si-TPP), by the deposition of atomic silicon onto a free-base TPP layer on a Ag(100) surface under ...ultrahigh vacuum (UHV) conditions. Scanning tunneling microscopy provides insights into the self-assembly of the TPP molecules before and after Si insertion. Silicon coordinates with all four nitrogen atoms of the TPP macrocycle and interacts with a silver atom of the substrate as confirmed by scanning tunneling spectroscopy, X-ray photoelectron spectroscopy, and complementary density functional theory calculations. The Si-TPP complex presents a saddle-shaped conformation that is stable under STM manipulation. Our study shows how protocols established for the on-surface metalation of tetrapyrroles can be adopted to achieve nonmetal porphyrins. Complementary experiments yielding Si-TPP and Ge-TPP on Ag(111) highlight the applicability to different main group elements and supports. The success of our nonmetal porphyrin synthesis procedure is further corroborated by a temperature-programmed desorption experiment, revealing the desorption of Ge-TPP. This extension of interfacial complex formation beyond metal elements opens promising prospects for new tetrapyrrole architectures with distinct properties and functionalities.
The controlled arrangement of N-heterocyclic carbenes (NHCs) on solid surfaces is a current challenge of surface functionalization. We introduce a strategy of using Ru porphyrins in order to control ...both the orientation and lateral arrangement of NHCs on a planar surface. The coupling of the NHC to the Ru porphyrin is a facile process which takes place on the interface: we apply NHCs as functional, robust pillars on well-defined, preassembled Ru porphyrin monolayers on silver and characterize these interfaces with atomic precision via a battery of experimental techniques and theoretical considerations. The NHCs assemble at room temperature modularly and reversibly on the Ru porphyrin arrays. We demonstrate a selective and complete functionalization of the Ru centers. With its binding, the NHC modifies the interaction of the Ru porphyrin with the Ag surface, displacing the Ru atom by 1 Å away from the surface. This arrangement of NHCs allows us to address individual ligands by controlled manipulation with the tip of a scanning tunneling microscope, creating patterned structures on the nanometer scale.
Complex landscapes with high topographic relief and intricate geometry present challenges for complete and accurate mapping of both lateral (x, y) and vertical (z) detail without deformation. ...Although small uninhabited/unmanned aerial vehicles (UAVs) paired with structure-from-motion (SfM) image processing has recently emerged as a popular solution for a range of mapping applications, common image acquisition and processing strategies can result in surface deformation along steep slopes within complex terrain. Incorporation of oblique (off-nadir) images into the UAV–SfM workflow has been shown to reduce systematic errors within resulting models, but there has been no consensus or documentation substantiating use of particular imaging angles. To address these limitations, we examined UAV–SfM models produced from image sets collected with various imaging angles (0–35°) within a high-relief ‘badland’ landscape and compared resulting surfaces with a reference dataset from a terrestrial laser scanner (TLS). More than 150 UAV–SfM scenarios were quantitatively evaluated to assess the effects of camera tilt angle, overlap, and imaging configuration on the precision and accuracy of the reconstructed terrain. Results indicate that imaging angle has a profound impact on accuracy and precision for data acquisition with a single camera angle in topographically complex scenes. Results also confirm previous findings that supplementing nadir image blocks with oblique images in the UAV–SfM workflow consistently improves spatial accuracy and precision and reduces data gaps and systematic errors in the final point cloud. Subtle differences among various oblique camera angles and imaging patterns suggest that higher overlap and higher oblique camera angles (20–35°) increased precision and accuracy by nearly 50% relative to nadir-only image blocks. We conclude by presenting four recommendations for incorporating oblique images and adapting flight parameters to enhance 3D mapping applications with UAV–SfM in high-relief terrain.
Efficient charge transfer across metal–organic interfaces is a key physical process in modern organic electronics devices, and characterization of the energy level alignment at the interface is ...crucial to enable a rational device design. We show that the insertion of alkali atoms can significantly change the structure and electronic properties of a metal–organic interface. Coadsorption of tetracyanoquinodimethane (TCNQ) and potassium on a Ag(111) surface leads to the formation of a two-dimensional charge transfer salt, with properties quite different from those of the two-dimensional Ag adatom TCNQ metal–organic framework formed in the absence of K doping. We establish a highly accurate structural model by combination of quantitative X-ray standing wave measurements, scanning tunnelling microscopy, and density-functional theory (DFT) calculations. Full agreement between the experimental data and the computational prediction of the structure is only achieved by inclusion of a charge-transfer-scaled dispersion correction in the DFT, which correctly accounts for the effects of strong charge transfer on the atomic polarizability of potassium. The commensurate surface layer formed by TCNQ and K is dominated by strong charge transfer and ionic bonding and is accompanied by a structural and electronic decoupling from the underlying metal substrate. The consequence is a significant change in energy level alignment and work function compared to TCNQ on Ag(111). Possible implications of charge-transfer salt formation at metal–organic interfaces for organic thin-film devices are discussed.
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