Occurrence of Aspartame in Foodstuffs in Cyprus and Relevant Risk Assessment

The basic aim of the control is to keep the levels of additives in foodstuffs and their dietary intake at safety levels and to cover: (i) Basic and frequently consumed foodstuffs in high quantities with emphasis to the food consumed by children, (ii) the toxicologically most important and most frequently used additives e.g. azodyes and synthetic sweeteners, (iii) previously known non complying samples and the information from RASFF system of EU. Aspartame is a very hot issue because there is a variety of reduced energy or sugar free foodstuffs that people can consume

Monte Carlo Deep Neural Network Model for Spread and Peak Prediction of COVID-19

Just a few days before the beginning of this year a new virus, widely known as the COVID-19, was detected in Wuhan, capital of the province Hubei, China. Since then, COVID-19 has spread all across the globe infecting more than half a million people resulting to the passing of nearly 25000 patients. Beside the social pain that this new pandemic is causing, the measures put in force to halt the spreading of the virus are stressing the global economy indicating a domino efect that can last even longer than the probable eradication of COVID-19. Yet, these measures are necessary to prevent health system reach their capacity, an occasion where di cult decisions will need to be made such as prioritization of patients to be treated

Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings

Characterization of disease-associated proteins improves our understanding of disease pathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on crossstrategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations

Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of –omics findings

Characterization of disease-associated proteins improves our understanding of disease pathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on cross-strategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations

CE–MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

BACKGROUND: Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management. METHODS: Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (nSig = 98; nnon-Sig = 445) and a validation set of 280 patients (nSig = 48, nnon-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm. RESULTS: Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set. CONCLUSIONS: This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics

Urinary peptide panel for prognostic assessment of bladder cancer relapse

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MSbased urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n=98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high signifcance in BC relapse prognosis [HR=5.76, p-value=0.0001, c-index=0.64]. Urinary peptide profles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC

Loss of AQP3 protein expression is associated with worse progression-free and cancer-specific survival in patients with muscle-invasive bladder cancer

Purpose Urothelial carcinoma has recently been shown to express several aquaporins (AQP), with AQP3 being of particular interest as its expression is reduced or lost in tumours of higher grade and stage. Loss of AQP3 expression was associated with worse progression-free survival (PFS) in patients with pT1 bladder cancer. The objective of this study was to investigate the prognostic value of AQP3 expression in patients with muscle-invasive bladder carcinoma (MIBC). Methods Retrospective single-centre analysis of the oncological outcome of patients following radical cystectomy (Cx) due to MIBC. Immunohistochemistry was used to assess AQP3 protein expression in 100 Cx specimens. Expression levels of AQP3 were related to clinicopathological variables. The impact of biomarker expression on progression-free, cancer-specific and overall survival was determined by multivariate Cox regression analysis (MVA). Results High expression of AQP3 by the tumour was associated with a statistically significantly improved PFS (75 vs. 19 %, p = 0.043) and CSS (75 vs. 18 %, p = 0.030) and, alongside lymph node involvement, was an independent predictor of PFS (HR 2.871, CI 1.066–7.733, p = 0.037), CSS (HR 3.325, CI 1.204–8.774, p = 0.019) and OS (HR 2.001, CI 1.014–3.947) in MVA. Conclusions Although the results of the study would be strengthened by a larger, more appropriately powered, prospective, multi-institutional study, our findings strongly suggest that AQP3 expression status may represent an independent predictor of PFS and CSS in MIBC and may help select patients in need for (neo-)adjuvant chemotherapy

EnvMine: A text-mining system for the automatic extraction of contextual information

<p>Abstract</p> <p>Background</p> <p>For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind.</p> <p>Results</p> <p>EnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved) of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings.</p> <p>Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude), thus allowing the calculation of distance between the individual locations.</p> <p>Conclusion</p> <p>EnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical variables of sampling sites, thus facilitating the performance of ecological analyses. EnvMine can also help in the development of standards for the annotation of environmental features.</p

Extending ontologies by finding siblings using set expansion techniques

Motivation: Ontologies are an everyday tool in biomedicine to capture and represent knowledge. However, many ontologies lack a high degree of coverage in their domain and need to improve their overall quality and maturity. Automatically extending sets of existing terms will enable ontology engineers to systematically improve text-based ontologies level by level