Association of Oxidative Stress Markers with Vascular Stiffness Parameters in Patients with Diabetic Neuropathy

Introduction: One of the most common chronic complications of diabetes mellitus is diabetic neuropathy. The aim of the study was to elucidate the association between selected markers of oxidative stress and markers of vascular stiffness and to contribute to the understanding of the pathophysiological links between oxidative stress and micro- and macrovascular complications of diabetes. Methods: We enrolled patients with type 2 DM (n = 49), with moderate to severe diabetic polyneuropathy of lower extremities, and a control group without microvascular complications (n = 29). The neuropathy group received alpha-lipoic acid infusion therapy. Sampling was performed before and after treatment to determine the level of oxidative markers (advanced glycation end-products—AGEs, glycation products of AOPP proteins, MDA malondialdehyde and oxidized LDL), parameters of metabolic control and parameters of vascular wall stiffness were measured by sphygmomanometry. Results: After the administration of alpha-lipoic acid, we demonstrated a significant reduction in the level of three selected oxidation markers (AOPP: p < 0.001, AGE: p < 0.001, oxLDL: p < 0.05). In contrast, the level of MDA did not change significantly (p = 0.83). Throughout the group, oxLDL was significantly correlated with central BP (SBP and DBP in the aorta, p < 0.05 and <0.01) and with the augmentation index (AiX/75 bpm, p < 0.01). AOPP significantly correlated with systolic BP in the aorta (p < 0.05). We did not find significant associations in the remaining oxidation markers. Conclusion: In our study, we demonstrated a reduction in the level of oxidative markers after alpha-lipoic acid administration and also an association between markers of oxidative damage to lipids and proteins (oxLDL and AOPP) and some parameters of vascular stiffness.

Acetylsalicylic Acid Reduces Passive Aortic Wall Stiffness and Cardiovascular Remodelling in a Mouse Model of Advanced Atherosclerosis

Acetylsalicylic acid (ASA) is widely used in secondary prevention of cardiovascular (CV) disease, mainly because of its antithrombotic effects. Here, we investigated whether ASA can prevent the progression of vessel wall remodelling, atherosclerosis, and CV complications in apolipoprotein E deficient (ApoE−/−) mice, a model of stable atherosclerosis, and in ApoE−/− mice with a mutation in the fibrillin-1 gene (Fbn1C1039G+/−), which is a model of elastic fibre fragmentation, accompanied by exacerbated unstable atherosclerosis. Female ApoE−/− and ApoE−/−Fbn1C1039G+/− mice were fed a Western diet (WD). At 10 weeks of WD, the mice were randomly divided into four groups, receiving either ASA 5 mg/kg/day in the drinking water (ApoE−/− (n = 14), ApoE−/−Fbn1C1039G+/− (n = 19)) or plain drinking water (ApoE−/− (n = 15), ApoE−/−Fbn1C1039G+/− (n = 21)) for 15 weeks. ApoE−/−Fbn1C1039G+/− mice showed an increased neutrophil–lymphocyte ratio (NLR) compared to ApoE−/− mice, and this effect was normalised by ASA. In the proximal ascending aorta wall, ASA-treated ApoE−/−Fbn1C1039G+/− mice showed less p-SMAD2/3 positive nuclei, a lower collagen percentage and an increased elastin/collagen ratio, consistent with the values measured in ApoE−/− mice. ASA did not affect plaque progression, incidence of myocardial infarction and survival of ApoE−/−Fbn1C1039G+/− mice, but systolic blood pressure, cardiac fibrosis and hypertrophy were reduced. In conclusion, ASA normalises the NLR, passive wall stiffness and cardiac remodelling in ApoE−/−Fbn1C1039G+/− mice to levels observed in ApoE−/− mice, indicating additional therapeutic benefits of ASA beyond its classical use.

THESEUS1 modulates cell wall stiffness and abscisic acid production in Arabidopsis thaliana

Plant cells can be distinguished from animal cells by their cell walls and high-turgor pressure. Although changes in turgor and the stiffness of cell walls seem coordinated, we know little about the mechanism responsible for coordination. Evidence has accumulated that plants, like yeast, have a dedicated cell wall integrity maintenance mechanism. It monitors the functional integrity of the wall and maintains integrity through adaptive responses induced by cell wall damage arising during growth, development, and interactions with the environment. These adaptive responses include osmosensitive induction of phytohormone production, defense responses, as well as changes in cell wall composition and structure. Here, we investigate how the cell wall integrity maintenance mechanism coordinates changes in cell wall stiffness and turgor in Arabidopsis thaliana. We show that the production of abscisic acid (ABA), the phytohormone-modulating turgor pressure, and responses to drought depend on the presence of a functional cell wall. We find that the cell wall integrity sensor THESEUS1 modulates mechanical properties of walls, turgor loss point, ABA biosynthesis, and ABA-controlled processes. We identify RECEPTOR-LIKE PROTEIN 12 as a component of cell wall integrity maintenance–controlling, cell wall damage–induced jasmonic acid (JA) production. We propose that THE1 is responsible for coordinating changes in turgor pressure and cell wall stiffness.

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

Links of Autoimmune Thyroid Disorders to Alzheimer’s Disease for Medicare Beneficiaries Ages 65+

To explore racial disparities in the interaction between Hashimoto thyroiditis (HT), Graves disease (GD), and Alzheimer’s disease (AD)we used age-adjusted rates for Medicare beneficiaries aged 65+. We investigated race/ethnicity/gender-specific trends of age-adjusted prevalence of HT and GD over 1991-2017 and used the Cox proportional hazards model with propensity score matching to explore the associations between AD and these thyroid disorders. The total age-adjusted prevalence of GD was increasing over the study period reaching a maximum of 0.35% in 2014 followed by a gradual decline. Except for relatively high prevalence rates in Native American Males, no statistically significant sex/race-related differences were identified. GD was found to be associated with an increased risk of AD onset [HR: 1.129; CI: 1.050-1.215]. The mechanism of interaction between thyroiditis and AD could follow several alternative pathways. The primary mechanism involves changes in blood vessel function, mostly arterial wall stiffness, which leads to increased pulse wave velocity and consequently to the higher amplitude of oscillation of the peripheral microcapillary system in the brain leading to cerebral leukoaraiosis and damage in kidney tissue. This, in turn, leads to kidney diseases (themselves associated with AD/ADRD), increased for HT and decreased for GD LDL cholesterol levels and an increased for HT and decreased for GD total/HDL cholesterol ratio, which has an important role in increased common carotid intima-media thickness (CIMT) in hypothyroid patients and is linked to arteriosclerosis. Additionally, both, endothelial dysfunction and arterial stiffness are risk factors for coronary or artery disease and consequently increase risks for AD/ADRD.

Relationship between the volume of perivascular adipose tissue and the vascular wall lesion

Aim. To study the relationship between the volume of perivascular adipose tissue (PVAT) and the vascular wall lesion.Material and methods. The study included 318 patients without cardiovascular disease (mean age, 63,5±13,7 years). Hypertension was detected in 268 (84,3%) patients. All patients underwent assessment of anthropometric characteristics, lipid profile, arterial wall stiffness with the estimation of cardio-ankle vascular index, intima-media thickness, brachial artery endothelial vasomotor function. Chest computed tomography was performed with the estimation of the volumes of PVAT and pericardial adipose tissue (PAT).Results. The volume of PVAT, on average, was 0,3 [0,2; 0,4] cm3 . The VAT volume was significantly higher in obese individuals when compared with patients with normal body weight: 0,4 [0,3; 0,5] vs 0,25 [0,2; 0,4] cm3 (p=0,0007). The VAT volume was higher in individuals with an increased CAVI level when compared with patients with normal CAVI values: 0,4 [0,3; 0,5] vs 0,3 [0,25; 0,3] (p=0,02). A significant correlation was found between the VAT volume and body mass index (r=0,27, p<0,005), waist circumference (r=0,41, p<0,005), CAVI (r=0,49, p<0,05), impaired endothelium-dependent brachial artery vasodilation (r=0,38, p<0,05). When performing multiple linear regression, a significant relationship of CAVI was found with age (β±SE, 0,51±0,15; p=0,002) and volume of PVAT (β±SE, 0,41±0,13; p=0,005).Conclusion. The results indicate the relationship of PVAT with visceral obesity and vascular wall stiffness parameters.

A cell wall-associated gene network shapes leaf boundary domains

Boundary domains delimit and organize organ growth throughout plant development almost relentlessly building plant architecture and morphogenesis. Boundary domains display reduced growth and orchestrate development of adjacent tissues in a non-cell autonomous manner. How these two functions are achieved remains elusive despite the identification of several boundary-specific genes. Here, we show using morphometrics at the organ and cellular levels that leaf boundary domain development requires SPINDLY (SPY), an O-fucosyltransferase, to act as cell growth repressor. Further we show that SPY acts redundantly with the CUP-SHAPED COTYLEDON transcription factors (CUC2 and CUC3), which are major determinants of boundaries development. Accordingly at the molecular level, CUC2 and SPY repress a common set of genes involved in cell wall loosening providing a molecular framework for the growth repression associated with boundary domains. Atomic force microscopy (AFM) confirmed that young leaf boundary domain cells have stiffer cell walls than marginal outgrowth. This differential cell wall stiffness was reduced in spy mutant. Taken together our data reveal a concealed CUC2 cell wall associated gene network linking tissue patterning with cell growth and mechanics.

Dependency of active pressure and equation of state on stiffness of wall

Autonomous motion and motility are hallmarks of active matter. Active agents, such as biological cells and synthetic colloidal particles, consume internal energy or extract energy from the environment to generate self-propulsion and locomotion. These systems are persistently out of equilibrium due to continuous energy consumption. It is known that pressure is not always a state function for generic active matter. Torque interaction between active constituents and confinement renders the pressure of the system a boundary-dependent property. The mechanical pressure of anisotropic active particles depends on their microscopic interactions with a solid wall. Using self-propelled dumbbells confined by solid walls as a model system, we perform numerical simulations to explore how variations in the wall stiffness influence the mechanical pressure of dry active matter. In contrast to previous findings, we find that mechanical pressure can be independent of the interaction of anisotropic active particles with walls, even in the presence of intrinsic torque interaction. Particularly, the dependency of pressure on the wall stiffness vanishes when the stiffness is above a critical level. In such a limit, the dynamics of dumbbells near the walls are randomized due to the large torque experienced by the dumbbells, leading to the recovery of pressure as a state variable of density.

Using Dynamic Pressure Response for Erosion Detection in Hydraulic Tubes and Hoses

Numerical simulations were performed to study the feasibility of erosion detection in hydraulic tubes and hoses using fluid dynamic pressure response analysis. Reflected pressure signals caused by wall thinning were studied to locate and quantify pipe defects. Simulations were conducted for steel pipes as well as hoses. Results showed that for a steel pipe, since the stiffness of the fluid is much less than the pipe material’s, a very big change of wall thickness is needed to have a meaningful change in wave propagation speed and therefore the dynamic pressure response. For hoses, the wall stiffness is much less than steel pipes, hence it is more feasible to detect changes in stiffness. A dataset of 10 000 dynamic pressure impulse responses from samples with randomly generated eroded geometries was calculated to train a gated recurrent unit (GRU) neural network. Results showed that under perfect conditions (no noise), we are able to detect an eroded section’s location, length, and change in wave propagation speed with relative errors of 2.69%, 4.88%, and 3.79%, respectively. The changes in the wave propagation speed was also categorized into three classes of low, mild, and severe erosion with the accuracy of 97.3%. Under more practical conditions including sensor noise, the accuracy of erosion detection is degraded, especially in the case of steel tubing. By retraining the model with noisy data, the drop in the accuracy is compensated to about 96%.