Tumor Dormancy: Implications for Invasion and Metastasis

Tumor dormancy refers to a critical stage of cancer development when tumor cells are present, but cancer does not progress. It includes both the concept of cellular dormancy, indicating the reversible switch of a cancer cell to a quiescent state, and that of tumor mass dormancy, indicating the presence of neoplastic masses that have reached cell population equilibrium via balanced growth/apoptosis rates. Tumor dormancy provides the conceptual framework, potentially explaining a major challenge in clinical oncology, tumor recurrence, which may occur years after cancer diagnosis. The mechanisms by which tumors are kept dormant, and what triggers their reawakening, are fundamental questions in cancer biology. It seems that a plethora of intracellular pathways and extracellular factors are involved in this process, rewiring the cells to plastically alter their metabolic and proliferative status. This phenomenon is highly dynamic in space and time. Mechanistic insights into both cellular and tumor dormancy have provided the rationale for targeting this otherwise stable period of cancer development, in order to prevent recurrence and maximize therapeutic benefit.     

A novel rapid method for the determination of frying oil quality: development of prototype and equations and examination with respect to legislation criteria

The present work presents a prototype apparatus developed for a novel method based on capillary penetration for the determination of frying oil quality and examines the method and the performance of the prototype. The work further assesses the method results in comparison with the results of established methods and proposes equations for calculating frying oil quality parameters relevant to legislation criteria. The examination of the method is performed by using 184 oil samples produced by frying different foods in different oils for forty-five successive batches. Equations relating the method output with the total polar compounds, total polymers and viscosity are proposed. High correlation coefficients between legislation criteria and the method result are obtained: R² = 0.96, R² = 0.91 and R² = 0.86 for correlation with the oil viscosity, the polymer compounds and the TPC of the fried oils, respectively. The false-positive and false-negative answers of the method are below 2.5% for all cases.     

Boosting algorithms in energy research: a systematic review

Neural Computing and Applications - Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the...     

Identifying Genetic Biomarkers Predicting Response to Anti-Vascular Endothelial Growth Factor Injections in Diabetic Macular Edema

Intraocular anti-vascular endothelial growth factor (VEGF) therapies are the front-line treatment for diabetic macular edema (DME); however, treatment response varies widely. This study aimed to identify genetic determinants associated with anti-VEGF treatment response in DME. We performed a genome-wide association study on 220 Australian patients with DME treated with anti-VEGF therapy, genotyped on the Illumina Global Screening Array, and imputed to the Haplotype Reference Consortium panel. The primary outcome measures were changes in central macular thickness (CMT in microns) and best-corrected visual acuity (BCVA in ETDRS letters) after 12 months. Association between single nucleotide polymorphism (SNP) genotypes and DME outcomes were evaluated by linear regression, adjusting for the first three principal components, age, baseline CMT/BCVA, duration of diabetic retinopathy, and HbA1c. Two loci reached genome-wide significance (p < 5 × 10⁻⁸) for association with increased CMT: a single SNP on chromosome 6 near CASC15 (rs78466540, p = 1.16 × 10⁻⁹) and a locus on chromosome 12 near RP11-116D17.1 (top SNP rs11614480, p = 2.69 × 10⁻⁸). Four loci were significantly associated with reduction in BCVA: two loci on chromosome 11, downstream of NTM (top SNP rs148980760, p = 5.30 × 10⁻⁹) and intronic in RP11-744N12.3 (top SNP rs57801753, p = 1.71 × 10⁻⁸); one near PGAM1P1 on chromosome 5 (rs187876551, p = 1.52 × 10⁻⁸); and one near TBC1D32 on chromosome 6 (rs118074968, p = 4.94 × 10⁻⁸). In silico investigations of each locus identified multiple expression quantitative trait loci and potentially relevant candidate genes warranting further analysis. Thus, we identified multiple genetic loci predicting treatment outcomes for anti-VEGF therapies in DME. This work may potentially lead to managing DME using personalized treatment approaches.     

Neurodegeneration and Inflammation—An Interesting Interplay in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder, caused by, so far, unknown pathogenetic mechanisms. There is no doubt that pro-inflammatory immune-mediated mechanisms are pivotal to the pathogenicity and progression of the disease. In this review, we highlight the binary role of microglia activation in the pathophysiology of the disorder, both neuroprotective and neuromodulatory. We present how the expression of several cytokines implicated in dopaminergic neurons (DA) degeneration could be used as biomarkers for PD. Viral infections have been studied and correlated to the disease progression, usually operating as trigger factors for the inflammatory process. The gut–brain axis and the possible contribution of the peripheral bowel inflammation to neuronal death, mainly dopaminergic neurons, seems to be a main contributor of brain neuroinflammation. The role of the immune system has also been analyzed implicating a-synuclein in the activation of innate and adaptive immunity. We also discuss therapeutic approaches concerning PD and neuroinflammation, which have been studied in experimental and in vitro models and data stemming from epidemiological studies.     

Size-dependent magnetic properties of single-crystalline multiferroic BiFeO3 nanoparticles

As-prepared, single-crystalline bismuth ferrite nanoparticles show strong size-dependent magnetic properties that correlate with: (a) increased suppression of the known spiral spin structure (period length of approximately 62 nm) with decreasing nanoparticle size and (b) uncompensated spins and strain anisotropies at the surface. Zero-field-cooled and field-cooled magnetization curves exhibit spin-glass freezing behavior due to a complex interplay between finite size effects, interparticle interactions, and a random distribution of anisotropy axes in our nanoparticle assemblies.     

Design of Efficiently Encodable Rate-Compatible LDPC Codes Using Vandermonde Extension Matrices

A low-complexity algorithm for the design of efficiently-encodable rate-compatible (RC) low-density parity-check (LDPC) codes by deterministically extending an irregular repeat-accumulate (IRA) is introduced. The extending structure is based on circulants shifted according a truncated Vandermonde matrix (VM) and therefore termed as “extended VM” (eVM). The novel extending algorithm is significantly less computationally complex than other known similar methods since it does not require any optimization of the extending profile or any post-construction girth conditioning. To improve the codes’ properties and correcting capabilities in low code rate applications, the optimal proportions of degree-1 and degree-2 parity bits for the extended nodes are investigated and, in contrast to existing deterministic extending approaches for RC-IRA codes, an extending increment step equal to half the information block length is chosen. Various bit error rate (BER) and frame error rate (FER) have been obtained for different code rates, R, and information block length k ₀ = 512 and 1024 bits considering an additive white Gaussian noise (AWGN) channel. The results have demonstrated that the proposed eVM RC-LDPC codes, despite their very simple structure and very low computational complexity, exhibit excellent performance only slightly inferior to both dedicated IRA and previously known RC-IRA codes for different data block sizes.     

Multi-platform data integration in microarray analysis

An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray platforms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among-platforms scale implemented by Shen et al. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.     

Phase equilibria of mixtures containing CO2 and organic acids using the UMR-PRU model

The UMR-PRU model, which has been successfully tested in the past to the predictions of different type of phase equilibrium and thermodynamic properties in binary and multicomponent systems, is applied in this work to phase equilibria in mixtures containing CO₂ and organic acids. New interaction parameters are determined by fitting only binary vapor–liquid equilibrium data and then they are used to predict the vapor–liquid, solid–gas and solid–liquid–gas equilibria in CO₂/organic acid systems. Furthermore, the UMR-PRU model with the newly derived interaction parameters is applied to the prediction of the phase equilibrium in ternary mixtures consisting of CO₂, organic acids and water. Satisfactory results are obtained in all cases.     

Designed spiro-bicyclic analogues targeting the ribosomal decoding center

The bacterial ribosome represents the confirmed biological target for many known antibiotics that interfere with bacterial protein synthesis. Aminoglycosides represent a lead paradigm in RNA molecular recognition and constitute ideal starting points for the design and synthesis of novel RNA binders. Previous rational design approaches of RNA-targeting small molecules have been mainly concentrated on direct functionalization of aminoglycosidic substructures. Herein, we successfully designed and synthesized rigid spirocyclic scaffolds locked in a predicted ribosome-bound "bioactive" conformation. These analogues are able to mimic many of the interactions of the natural products for the A-site, as proven by their obtained binding affinities. The development of an optimized approach for their synthesis and their potential to inhibit protein production in vitro are presented. Our results could be further utilized for the development of analogues with improved antibiotic profiles.