Engbergandresen1333

Currently, it is still unclear how and to what extent a change in temperature impacts the relative contributions of coherent and incoherent phonons to thermal transport in superlattices. Some seemingly conflicting computational and experimental observations of the temperature dependence of lattice thermal conductivity make the coherent-incoherent thermal transport behaviors in superlattices even more elusive. In this work, we demonstrate that incoherent phonon contribution to thermal transport in superlattices increases as the temperature increases due to elevated inelastic interfacial transmission. On the other hand, the coherent phonon contribution decreases at higher temperatures due to elevated anharmonic scattering. The competition between these two conflicting mechanisms can lead to different trends of lattice thermal conductivity as temperature increases, i.e. increasing, decreasing, or non-monotonic. Finally, we demonstrate that the neural network-based machine learning model can well capture the coherent-incoherent transition of lattice thermal transport in the superlattice, which can greatly aid the understanding and optimization of thermal transport properties of superlattices.

Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. selleck chemical The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.

To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.

The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.

The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD.

The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD.Competing endogenous RNA (ceRNA) pathways play pivotal roles in the formation and progression of gastric cancer (GC). Employing multi-omics analysis, we sought to identify a ceRNA network associated with GC progression. We analyzed3Gene Expression Omnibus datasets as well as data from The Cancer Genome Atlas to identify genes that were differentially expressed in GC tissues. A total of 84 upregulated genes and 106 downregulated genes were found. Enrichment analysis indicated that some pathways were strongly linked with tumor formation and progression. We also screened hub genes to establish a lncRNA-miRNA-mRNA network. We ultimately identified 8 hub genes, 6 key miRNAs and 4 key lncRNAs that interact within a common ceRNA network. Correlation analysis and in vitro experiments were conducted to verify the regulatory effect of the ceRNA network in GC. A knockdown assay confirmed that the DLGAP1-AS1/miR-203a-3p/THBS2 axis is a ceRNA network involved in GC progression. In this study, we elucidated the role of the DLGAP1-AS1/miR-203a-3p/THBS2 ceRNA network in the progression of GC. These molecules maybe evaluated as therapeutic targets and prognostic biomarkers for GC.Coronavirus disease 19 (COVID-19) is currently a global pandemic that affects patients with other pathologies. Here, we investigated the influence of treatments for osteoporosis and other non-inflammatory rheumatic conditions, such as osteoarthritis and fibromyalgia, on COVID-19 incidence. To this end, we conducted a cross-sectional study of 2,102 patients being treated at the Rheumatology Service of Hospital del Mar (Barcelona, Spain). In our cohort, COVID-19 cumulative incidence from March 1 to May 3, 2020 was compared to population estimates for the same city. We used Poisson regression models to determine the adjusted relative risk ratios for COVID-19 associated with different treatments and comorbidities. Denosumab, zoledronate and calcium were negatively associated with COVID-19 incidence. Some analgesics, particularly pregabalin and most of the studied antidepressants, were positively associated with COVID-19 incidence, whereas duloxetine presented a negative association. Oral bisphosphonates, vitamin D, thiazide diuretics, anti-hypertensive drugs and chronic non-steroidal anti-inflammatory drugs had no effect on COVID-19 incidence in the studied population. Our results provide novel evidence to support the maintenance of the main anti-osteoporosis treatments in COVID-19 patients, which may be of particular relevance to elderly patients affected by the SARS-CoV-2 pandemic.In this study, we investigated the functional and clinical significance of the long non-coding RNA (lncRNA) FAM181A-AS1 in human gliomas. TCGA, GTEx and CGGA database analyses showed that high FAM181A-AS1 expression correlates with advanced tumor stage and poor survival of glioma patients. FAM181A-AS1 expression is higher in glioma cell lines compared to normal human astrocytes (NHA). CCK-8, EdU, and colony formation assays show that FAM181A-AS1 knockdown decreases proliferation and colony formation in glioma cells, whereas, FAM181A-AS1 overexpression reverses these effects. Bioinformatics analysis showed that miR-129-5p is a potential target of FAM181A-AS1. MiR-129-5p expression negatively correlates with FAM181A-AS1 expression in glioma patients. Dual luciferase reporter assays confirmed that miR-129-5p binds directly to FAM181A-AS1 in glioma cells. RNA immunoprecipitation (RIP) assays using anti-Ago2 antibody pulled down FAM181A-AS1 with miR-129-5p. Bioinformatics analysis identified ZRANB2 as a potential miR-129-5p target gene.