Polyphenol characterization and encapsulation in β-cyclodextrin of a flavonoid-rich Hypericum perforatum (St John's wort) extract

Purified methanolic extracts of Hypericum perforatum (HP) from Northern Greece were very rich in flavonoids. Among simple polyphenols determined by GC-MS, epicatechin, catechin and quercetin predominated, their concentrations being 118.9 ± 20.6, 8.7 ± 1.4 and 5.8 ± 0.8 mg/g extract. LC-MS analysis revealed that the HP extract was mainly consisted of quercetin glucosides, catechin and quercetin. Among anthocyanins, malvidine was present at 1.96 ± 0.2 mg/g. The 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) and the ferric reducing antioxidant power (FRAP) assays showed that the HP extract exerted significant antioxidant activity. The inclusion complex of HP with β-cyclodextrin (β-CD) was prepared by mixing 1:4 mass ratios of its components in aqueous media and subsequent freeze-drying. The encapsulation was verified by differential scanning calorimetry (DSC) and NMR studies, and encapsulation efficiencies were 27.5, 30.0 and 35.0% for catechin, epicatechin and quercetin respectively. DSC after thermal oxidation indicated that the inclusion complex remained intact at temperatures where the free HP extract was oxidized. It is concluded that the encapsulation in β-CD improves the thermal stability of nutraceutical antioxidants present in St John's wort extract, suggesting that the inclusion complex could serve as a flavonoids-rich food supplement or a novel additive to enhance the antioxidant capacity of fresh or thermally processed food.     

Development of an experimental procedure for real time investigation of diffusion in foods

Diffusion and mass transfer are ubiquitous operations in food processing. The application of electric fields can influence the mass transfer properties of foods. A small scale processing unit was used in the development of a method that would allow real-time measurement of diffusion of dyes into gel samples (at a range of temperatures and electric field strengths). The depth of penetration and localisation of infused material can be visualised and measured using image analysis methods.     

Molecular Drivers of Developmental Arrest in the Human Preimplantation Embryo: A Systematic Review and Critical Analysis Leading to Mapping Future Research

Developmental arrest of the preimplantation embryo is a multifactorial condition, characterized by lack of cellular division for at least 24 hours, hindering the in vitro fertilization cycle outcome. This systematic review aims to present the molecular drivers of developmental arrest, focusing on embryonic and parental factors. A systematic search in PubMed/Medline, Embase and Cochrane-Central-Database was performed in January 2021. A total of 76 studies were included. The identified embryonic factors associated with arrest included gene variations, mitochondrial DNA copy number, methylation patterns, chromosomal abnormalities, metabolic profile and morphological features. Parental factors included, gene variation, protein expression levels and infertility etiology. A valuable conclusion emerging through critical analysis indicated that genetic origins of developmental arrest analyzed from the perspective of parental infertility etiology and the embryo itself, share common ground. This is a unique and long-overdue contribution to literature that for the first time presents an all-inclusive methodological report on the molecular drivers leading to preimplantation embryos’ arrested development. The variety and heterogeneity of developmental arrest drivers, along with their inevitable intertwining relationships does not allow for prioritization on the factors playing a more definitive role in arrested development. This systematic review provides the basis for further research in the field.     

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).     

Resonant-based identification of the elastic properties of layered materials: Application to air-plasma sprayed thermal barrier coatings

This work introduces a resonant-based, mixed numerical–experimental method for the determination of the in-plane elastic properties of the constituent materials of laminates. This non-destructive method identifies elastic properties from the resonant frequencies of beam-shaped layered specimens, using a set of finite element models. The method is demonstrated on a thermal barrier coating system made of NiCoCrAlY bondcoat and yttria-stabilised zirconia topcoat deposited by air-plasma spraying on stainless steel. The stainless steel was found to be elastically anisotropic, while both bondcoat and topcoat exhibited in-plane isotropy. Moreover, the topcoat Poisson's ratio approached zero, and the bondcoat properties varied with the coating thickness. Scanning electron microscopy was used to correlate the identified elastic properties with the coating microstructure.     

Serum Proteome Signatures of Anti-SARS-CoV-2 Vaccinated Healthcare Workers in Greece Associated with Their Prior Infection Status

Over the course of the pandemic, proteomics, being in the frontline of anti-COVID-19 research, has massively contributed to the investigation of molecular pathogenic properties of the virus. However, data on the proteome on anti-SARS-CoV-2 vaccinated individuals remain scarce. This study aimed to identify the serum proteome characteristics of anti-SARS-CoV-2 vaccinated individuals who had previously contracted the virus and comparatively assess them against those of virus-naïve vaccine recipients. Blood samples of n = 252 individuals, out of whom n = 35 had been previously infected, were collected in the “G. Gennimatas” General Hospital of Thessaloniki, from 4 January 2021 to 31 August 2021. All participants received the BNT162b2 mRNA COVID-19 vaccine (Pfizer/BioNTech). A label-free quantitative proteomics LC-MS/MS approach was undertaken, and the identified proteins were analyzed using the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes) databases as well as processed by bioinformatics tools. Titers of total RBD-specific IgGs against SARS-CoV-2 were also determined using the SARS-CoV-2 IgG II Quant assay. A total of 47 proteins were significantly differentially expressed, the majority of which were down-regulated in sera of previously infected patients compared to virus-naïve controls. Several pathways were affected supporting the crucial role of the humoral immune response in the protection against SARS-CoV-2 infection provided by COVID-19 vaccination. Overall, our comprehensive proteome profiling analysis contributes novel knowledge of the mechanisms of immune response induced by anti-SARS-CoV-2 vaccination and identified protein signatures reflecting the immune status of vaccine recipients.     

Comparison of block matching and differential methods for motion analysis of the carotid artery wall from ultrasound images

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF (HS)), weighted least-squares optical flow (OF (LK(WLS))), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF (LK(WLS)) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF (HS) (11.57 pixels). Due to its superior performance, OF (LK(WLS)) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF (LK(WLS)) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.