Electrochemical Impedance Imaging via the Distribution of Diffusion Times

We develop a mathematical framework to analyze electrochemical impedance spectra in terms of a distribution of diffusion times (DDT) for a parallel array of random finite-length Warburg (diffusion) or Gerischer (reaction-diffusion) circuit elements. A robust DDT inversion method is presented based on Complex Nonlinear Least Squares (CNLS) regression with Tikhonov regularization and illustrated for three cases of nanostructured electrodes for energy conversion: (i) a carbon nanotube supercapacitor, (ii) a silicon nanowire Li-ion battery, and (iii) a porous-carbon vanadium flow battery. The results demonstrate the feasibility of non-destructive "impedance imaging" to infer microstructural statistics of random, heterogeneous materials

Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion Algorithms

We introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1) the representation of unknown distributions, (2) the minimization of a metric of error to estimate parameters arising from the chosen representation, subject to constraints on (3) the complexity control parameters, and (4) a means for choosing optimal control parameter values. These features must be present to overcome the ill-posed nature of EIS inversion problems. We review three established EIS inversion algorithms to illustrate the pervasiveness of these features, and show the utility of the framework by resolving ambiguities concerning three more algorithms. Our framework is then used to design the generalized EIS inversion (gEISi) algorithm, which uses Gaussian basis function representation, modality control parameter, and cross-validation for choosing the optimal control parameter value. The gEISi algorithm is applicable to the generalized EIS inversion problem, which allows for a wider range of underlying models. We also considered the construction of credible intervals for distributions arising from the algorithm. The algorithm is able to accurately reproduce distributions which have been difficult to obtain using existing algorithms. It is provided gratis on the repository https://github.com/suryaeff/gEISi.git.Comment: 46 pages, to be submitted to the Journal of the Electrochemical Societ

The Glymphatic System in Diabetes-Induced Dementia

The glymphatic system has emerged as an important player in central nervous system (CNS) diseases, by regulating the vasculature impairment, effectively controlling the clearance of toxic peptides, modulating activity of astrocytes, and being involved in the circulation of neurotransmitters in the brain. Recently, several studies have indicated decreased activity of the glymphatic pathway under diabetes conditions such as in insulin resistance and hyperglycemia. Furthermore, diabetes leads to the disruption of the blood-brain barrier and decrease of apolipoprotein E (APOE) expression and the secretion of norepinephrine in the brain, involving the impairment of the glymphatic pathway and ultimately resulting in cognitive decline. Considering the increased prevalence of diabetes-induced dementia worldwide, the relationship between the glymphatic pathway and diabetes-induced dementia should be investigated and the mechanisms underlying their relationship should be discussed to promote the development of an effective therapeutic approach in the near future. Here, we have reviewed recent evidence for the relationship between glymphatic pathway dysfunction and diabetes. We highlight that the enhancement of the glymphatic system function during sleep may be beneficial to the attenuation of neuropathology in diabetes-induced dementia. Moreover, we suggest that improving glymphatic system activity may be a potential therapeutic strategy for the prevention of diabetes-induced dementia

miR-Let7A Controls the Cell Death and Tight Junction Density of Brain Endothelial Cells under High Glucose Condition

Hyperglycemia-induced stress in the brain of patients with diabetes triggers the disruption of blood-brain barrier (BBB), leading to diverse neurological diseases including stroke and dementia. Recently, the role of microRNA becomes an interest in the research for deciphering the mechanism of brain endothelial cell damage under hyperglycemia. Therefore, we investigated whether mircoRNA Let7A (miR-Let7A) controls the damage of brain endothelial (bEnd.3) cells against high glucose condition. Cell viability, cell death marker expressions (p-53, Bax, and cleaved poly ADP-ribose polymerase), the loss of tight junction proteins (ZO-1 and claudin-5), proinflammatory response (interleukin-6, tumor necrosis factor-α), inducible nitric oxide synthase, and nitrite production were confirmed using MTT, reverse transcription-PCR, quantitative-PCR, Western blotting, immunofluorescence, and Griess reagent assay. miR-Let7A overexpression significantly prevented cell death and loss of tight junction proteins and attenuated proinflammatory response and nitrite production in the bEnd.3 cells under high glucose condition. Taken together, we suggest that miR-Let7A may attenuate brain endothelial cell damage by controlling cell death signaling, loss of tight junction proteins, and proinflammatory response against high glucose stress. In the future, the manipulation of miR-Let7A may be a novel solution in controlling BBB disruption which leads to the central nervous system diseases

Transcriptomic Analysis of High Fat Diet Fed Mouse Brain Cortex

High fat diet can lead to metabolic diseases such as obesity and diabetes known to be chronic inflammatory diseases with high prevalence worldwide. Recent studies have reported cognitive dysfunction in obese patients is caused by a high fat diet. Accordingly, such dysfunction is called “type 3 diabetes” or “diabetic dementia.” Although dysregulation of protein-coding genes has been extensively studied, profiling of non-coding RNAs including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) has not been reported yet. Therefore, the objective of this study was to obtain profiles of diverse RNAs and determine their patterns of alteration in high fat fed brain cortex compared to normal brain cortex. To investigate regulatory roles of both coding and non-coding RNAs in high fat diet brain, we performed RNA sequencing of ribosomal RNA-depleted RNAs and identified genome-wide lncRNAs and circRNAs expression and co-expression patterns of mRNAs in high fat diet mouse brain cortex. Our results showed expression levels of mRNAs related to neurogenesis, synapse, and calcium signaling were highly changed in high fat diet fed cortex. In addition, numerous differentially expressed lncRNAs and circRNAs were identified. Our study provides valuable expression profiles and potential function of both coding and non-coding RNAs in high fat diet fed brain cortex

The role of melatonin in the onset and progression of type 3 diabetes

Abstract Alzheimer’s disease (AD) is defined by the excessive accumulation of toxic peptides, such as beta amyloid (Aβ) plaques and intracellular neurofibrillary tangles (NFT). The risk factors associated with AD include genetic mutations, aging, insulin resistance, and oxidative stress. To date, several studies that have demonstrated an association between AD and diabetes have revealed that the common risk factors include insulin resistance, sleep disturbances, blood brain barrier (BBB) disruption, and altered glucose homeostasis. Many researchers have discovered that there are mechanisms common to both diabetes and AD. AD that results from insulin resistance in the brain is termed “type 3 diabetes”. Melatonin synthesized by the pineal gland is known to contribute to circadian rhythms, insulin resistance, protection of the BBB, and cell survival mechanisms. Here, we review the relationship between melatonin and type 3 diabetes, and suggest that melatonin might regulate the risk factors for type 3 diabetes. We suggest that melatonin is crucial for attenuating the onset of type 3 diabetes by intervening in Aβ accumulation, insulin resistance, glucose metabolism, and BBB permeability