Foundation for Research and Technology - Hellas

Secondary fracture prevention is an essential part of hip fracture treatment. Despite this, many patients are discharged without the appropriate anti-osteoporotic medication. The aim of this study is to report the outcomes of the application of an in-hospital, surgeon-led anti-osteoporotic medication algorithm to patients with hip fractures. This prospective cohort study followed patients with hip fractures who were treated at a tertiary referral hospital between 2020 and 2022. At discharge, anti-osteoporotic medication according to the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation algorithm was prescribed to all patients. Multivariate Cox regression analysis was used to investigate the risks of non-persistence to medication and of secondary fracture. Two hundred thirteen consecutive patients were prospectively followed. Mean follow-up was 17.2 ± 7.1 months. Persistence to medication at 2 years was 58% (95%CI 51–65%). A secondary osteoporotic fracture occurred in 1/126 (0.8%) persistent patients and 9/87 (11.4%) non-persistent patients. Multivariable Cox regression analysis confirmed that persistence to medication was significantly associated with a lower risk of secondary fracture (cause-specific hazard ratio [csHR] 0.05; 95%CI 0.01–0.45; p = 0.007). The application of the surgeon-led AO Foundation algorithm enables the in-hospital initiation of anti-osteoporotic treatment, leading to better persistence to medication and decreased incidence of secondary osteoporotic fractures.

Diatoms are eukaryotic microalgae responsible for nearly half of the marine productivity. RNA interference (RNAi) is a mechanism of regulation of gene expression mediated by small RNAs (sRNAs) processed by the endoribonuclease Dicer (DCR). To date, the mechanism and physiological role of RNAi in diatoms are unknown. We mined diatom genomes and transcriptomes for key RNAi effectors and retraced their phylogenetic history. We generated DCR knockout lines in the model diatom species Phaeodactylum tricornutum and analyzed their mRNA and sRNA populations, repression‐associated histone marks, and acclimatory response to nitrogen starvation. Diatoms presented a diversification of key RNAi effectors whose distribution across species suggests the presence of distinct RNAi pathways. P. tricornutum DCR was found to process 26–31‐nt‐long double‐stranded sRNAs originating mostly from transposons covered by repression‐associated epigenetic marks. In parallel, P. tricornutum DCR was necessary for the maintenance of the repression‐associated histone marks H3K9me2/3 and H3K27me3. Finally, PtDCR‐KO lines presented a compromised recovery post nitrogen starvation suggesting a role for P. tricornutum DCR in the acclimation to nutrient stress. Our study characterized the molecular function of the single DCR homolog of P. tricornutum suggesting an association between RNAi and heterochromatin maintenance in this model diatom species.

Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems.

Optimal sampling period selection for high-frequency data is at the core of financial instruments based on algorithmic trading. The unique features of such data, absent in data measured at lower frequencies, raise significant challenges to their statistical analysis and econometric modelling, especially in the case of heavy-tailed data exhibiting outliers and rare events much more frequently. To address this problem, this paper proposes a new methodology for optimal sampling period selection, which better adapts to heavy-tailed statistics of high-frequency financial data. In particular, the novel concept of the degree of impulsiveness (DoI) is introduced first based on alpha-stable distributions, as an alternative source of information for characterising a broad range of impulsive behaviours. Then, a DoI-based generalised volatility signature plot is defined, which is further employed for determining the optimal sampling period. The performance of our method is evaluated in the case of risk quantification for high-frequency indexes, demonstrating a significantly improved accuracy when compared against the well-established volatility-based approach.

Context. KS 1947+300 is a Be/X-ray binary. Despite its nearly circular orbit, it displays both giant and regular less intense X-ray outbursts. According to the viscous decretion disk model, such low eccentric binaries should not show periodic outbursts. Aims. We have studied the long-term optical variability of KS 1947+300 and its relationship with X-ray activity. Our objective is to investigate the origin of this variability. Methods. We have analyzed data covering more than 20 years of observations. In the optical band, we have analyzed spectra and light curves. We measured the strength of the Hα and He I 6678 Å lines. In the X-ray band, we studied the long-term light curves provided by several all-sky monitors. Results. KS 1947+300 exhibits changes in brightness and Hα emission on time scales from months to years. The optical and IR variability shows small amplitude changes during the active X-ray state, and a long, smooth decrease during the quiescent state. The fact that the amplitude of variability increased with wavelength suggests that the long-term decrease of the optical emission is due to the weakening of the circumstellar disk. Structural changes in the disk may also be the origin of the periodic signals with periods ∼ 200 days detected in the ZTF-g and r band light curves. We speculate that these changes are related to the mechanism that ejects matter from the photosphere of the Be star into the disk. We have also studied the X-ray variability that manifested as a series of type I outbursts after the two giant outburst in 2000 and 2013 and found that the intensity and peak orbital phase differ from outburst to outburst. The type I outbursts in KS 1947+300 are not strictly periodic. This irregularity could result from precession of the interacting points between the neutron star and the disk, namely the disk apastron and the two nodes of the disk. Conclusions. The long-term changes in optical continuum and line emission and the X-ray variability patterns are attributed to an evolving and distorted decretion disk.

Objective: This study aims to assess the value of biomarker based radiomics to predict IDH mutation in gliomas. The patient cohort consists of 160 patients histopathologicaly proven of primary glioma (WHO grades 2–4) from 3 different centers. Methods: To quantify the DSC perfusion signal two different mathematical modeling methods were used (Gamma fitting, leakage correction algorithms) considering the assumptions about the compartments contributing in the blood flow between the extra- and intra vascular space. Results: The Mean slope of increase (MSI) and the K1 parameter of the bidirectional exchange model exhibited the highest performance with (ACC 74.3% AUROC 74.2%) and (ACC 75% AUROC 70.5%) respectively. Conclusion: The proposed framework on DSC-MRI radiogenomics in gliomas has the potential of becoming a reliable diagnostic support tool exploiting the mathematical modeling of the DSC signal to characterize IDH mutation status through a more reproducible and standardized signal analysis scheme for facilitating clinical translation.

A notable application of polymeric nanocomposites is the design of water vapor permeable (WVP) membranes. "Breathable" membranes can be created by the incorporation of micro/nanofillers, such as CaCO 3 , that interrupt the continuity of the polymeric phase and when subjected to additional uniaxial or biaxial stretching this process leads to the formation of micro/nanoporous structures. Among the candidate nanofillers, carbon nanotubes (CNTs) have demonstrated excellent intrinsic WVP properties. In this study, chemically modified MWCNTs with oligo olefin-type groups (MWCNT-g-PP) are incorporated by melt processes into a PP matrix; a β-nucleating agent (β-NA) is also added. The crystallization behavior of the nanocomposite films is evaluated by differential scanning calorimetry (DSC) and X-ray diffraction (XRD). The WVP performance of the films is assessed via the "wet" cup method. The nanohybrid systems, incorporating both MWCNT-g-PP and β-NA, exhibit enhanced WVP compared to films containing only MWCNT-g-PP or β-NA. This improvement can be attributed to the significant increase in the growth of α-type crystals taking place at the edges of the CNTs. This increased crystal growth exerts a form of stress on the metastable β-phase, thereby expanding the initial microporosity. In parallel, the coexistence of the inherently water vapor-permeable CNTs, further enhances the water vapor permeability reaching a specific water vapor transmission rate (Sp.WVTR) of 5500 µm.g/m 2 .day in the hybrid composite compared to 1000 µm.g/m 2 .day in neat PP. Notably, the functionalized MWCNT-g-PP used as nanofiller in the preparation of the "breathable" PP films demonstrated no noteworthy cytotoxicity levels within the low concentration range used, an important factor in terms of sustainability.

Cellular condensates are usually ribonucleoprotein assemblies with liquid- or solid-like properties. Because these subcellular structures lack a delineating membrane, determining their compositions is difficult. Here we describe a proximity-biotinylation approach for capturing the RNAs of the condensates known as processing bodies (PBs) in Arabidopsis (Arabidopsis thaliana). By combining this approach with RNA detection, in silico and high-resolution imaging approaches, we studied PBs under normal conditions and heat stress. PBs showed a much more dynamic RNA composition than the total transcriptome. RNAs involved in cell wall development and regeneration, plant hormonal signaling, secondary metabolism/defense, and RNA metabolism were enriched in PBs. RNA binding proteins and the liquidity of PBs modulated RNA recruitment, while RNAs were frequently recruited together with their encoded proteins. In PBs, RNAs follow distinct fates: in small liquid–like PBs, RNAs get degraded while in more solid–like larger ones, they are stored. PB properties can be regulated by the actin-polymerizing SCAR (suppressor of the cyclic AMP (cAMP))–WAVE (WASP family verprolin homologous) complex. SCAR/WAVE modulates the shuttling of RNAs between PBs and the translational machinery, thereby adjusting ethylene signaling. In summary, we provide an approach to identify RNAs in condensates that allowed us to reveal a mechanism for regulating RNA fate.

Nowadays, nanoscience and nanotechnology depict cutting-edge areas of modern science and technology across an array of applications, including heterogeneous catalysis [...]

Geranylgeranyl pyrophosphate (diphosphate) synthase (GGPPS) plays an important role in various physiological processes in insects, such as isoprenoid biosynthesis and protein prenylation. Here, we functionally characterised the GGPPS from the major agricultural lepidopteran pests Spodoptera frugiperda and Helicoverpa armigera . Partial disruption of GGPPS by CRISPR in S. frugiperda decreased embryo hatching rate and larval survival, suggesting that this gene is essential. Functional expression in vitro of Helicoverpa armigera GGPPS in Escherichia coli revealed a catalytically active enzyme. Next, we developed and optimised an enzyme assay to screen for potential inhibitors, such as the zoledronate and the minodronate, which showed a dose‐dependent inhibition. Phylogenetic analysis of GGPPS across insects showed that GGPPS is highly conserved but also revealed several residues likely to be involved in substrate binding, which were substantially different in bee pollinator and human GGPPS. Considering the essentiality of GGPPS and its putative binding residue variability qualifies a GGPPS as a novel pesticide target. The developed assay may contribute to the identification of novel insecticide leads.

Using LIBS for the analysis of archaeological and geological marine mollusc shells is a growing research area that relies on customised instrumentation and specific workflows that can accommodate the variety and precision of the required sampling parameters. However, the increased efficiency offered by LIBS, which enables the study of a larger quantity of shell samples for temperature variation, ecological parameters, and human consumption practices, outweighs the initial efforts required to develop customised instrumentation and workflows. In this work, we present detailed specifications and parameters for the development of a LIBS system capable of generating Mg/Ca images on marine shells that directly correlate with seasonal sea temperatures. Our main objective was to develop specifications that enable easy adaptation of LIBS systems to existing laboratories for studying hard-tissue samples. These specifications were used to develop a customised micro-LIBS system and apply it to a real-world example of an archaeological study to better understand its efficiency on the marine mollusc shells and demonstrate its potential for broader applications in interdisciplinary research. In total 101 shell specimens have been analysed within a time frame of approximately 71 h of machine time, producing 234 images (100 µm resolution: 100 images, 30 µm resolution: 134 images). SEM analysis of the irradiated sections of the shell revealed a primary ablated area of 10–15 µm in diameter, while a secondary affected area of the shell’s crystal fabric extended to 30–50 µm after repeated shots. Overall, this new customised system reliably and efficiently analysed marine mollusc specimens without major destructive effects, enabling additional analyses for other proxies to be carried out. This study highlights the potential of the LIBS method for interdisciplinary research, encompassing applications in paleoclimatology, marine ecology, and archaeology.

Introduction Pediatric obesity has steadily increased in recent decades. Large-scale genome-wide association studies (GWAS) conducted primarily in Eurocentric adult populations have identified approximately 100 loci that predispose to obesity and type II diabetes. GWAS in children and individuals of non-European descent, both disproportionately affected by obesity, are fewer. Rare syndromic and monogenic obesities account for only a small portion of childhood obesity, so understanding the role of other genetic variants and their combinations in heritable obesities is key to developing targeted and personalized therapies. Tight and responsive regulation of the cAMP-dependent protein kinase (PKA) signaling pathway is crucial to maintaining healthy energy metabolism, and mutations in PKA-linked genes represent the most common cause of monogenic obesity. Methods For this study, we performed targeted exome sequencing of 53 PKA signaling-related genes to identify variants in genomic DNA from a large, ethnically diverse cohort of obese or metabolically challenged youth. Results We confirmed 49 high-frequency variants, including a novel variant in the PDE11A gene (c.152C>T). Several other variants were associated with metabolic characteristics within ethnic groups. Discussion We conclude that a PKA pathway-specific variant search led to the identification of several new genetic associations with obesity in an ethnically diverse population.

Background Cardiovascular and cerebrovascular disorders, which are now the leading and second causes of death in the majority of the world's countries, affect human life and health. Moreover, the prediction of death caused by these is deemed necessary to prevent it and reduce its rate. Purpose We employ machine learning models for the prediction of cardiovascular and/or cerebrovascular death within 7 years follow-up using clinical and laboratory features. Methods We analyzed the dataset of German epidemiological trial on ankle brachial index (getABI) study, including 5,587 patients (mean age 71.14 years, 40.7 % male, 359 deceased patients and 5,228 alive patients). Recursive feature elimination with cross-validation selected 11 features from a set of 55 features: diabetes mellitus, ABI index before exercise, vitamin D, troponin I, triglycerides, potassium, LDL cholesterol, HDL cholesterol, the flow noise of the right and left carotid artery and the pulse status of the right popliteal artery. Additionally, the handling of missing values was achieved by SimpleImputer. Random Forest (RF), Adaptive Boosting (AdaBoost), LightGBM classifiers were applied to predict death and were trained and tested using 5-fold cross-validation. Moreover, GridSearchCV tuned the hyperparameters of the models. Results LightGBM was the most accurate model, achieving 87.56 % mean balanced accuracy and 90.63 % mean value of the area under (AUC) the Receiver Operating Characteristic Curve (ROC). The mean ROC-AUC value of the RF, and AdaBoost were equal to 87.25 % and 89.52 %, respectively. Τhe mean ROC-AUC value, its standard deviation and the mean balanced accuracy of the models are presented in Figure 1. Moreover, Figure 2 indicates the ROC-AUC values for each k-fold and the mean value of them which achieved by the best classifier, the LightGBM. Conclusion Machine learning models achieved accurate prediction of cardiovascular and cerebrovascular death in 5,587 patients within 7 years follow-up utilizing basic medical data.