FTIR-ATR, FT-RAMAN and GC-FID assessment of discrimination of aged wine spirits under different conditions

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A new perspective on vescalagin, castalagin, and their degadation pathways in wine spirits ageing

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Safety of etanercept in the treatment of rheumatic disease patients with Hepatitis C virus infection

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Volatile phenols in aged wine spirits: role, contents and impact of ageing systems

The volatile phenols (eugenol, guaiacol, 4-methylguaiacol, syringol, 4-methylsyringol and 4-allylsyringol) are odorant compounds that may exist in aged wine spirits resulting from their contact with wooden barrels during the ageing process. These compounds, which are originated from wood lignin’s, revealed an important sensory impact in aged wine spirits due to their low sensory thresholds and correlations with sensory attributes such as woody, toasted, smoke, which have a close relationship with the quality of these beverages. The wine spirits are traditionally aged in wooden barrels but the use of wood fragments, with or without micro-oxygenation, is a technological alternative that has been recently studied by our team with promising results. This work presents an overview of volatile phenols’ amounts in wine spirits aged in wooden barrels during different ageing times and using two kinds of wood (chestnut versus oak). These compounds were quantified by GC-FID, after a previous extraction and concentration steps, and their identification was assessed by GC-MS. It is also examined the results and the impact of alternative technologies on the amounts of such compounds. The ANOVA results showed a significant effect of the ageing system and the wood botanical species on the volatile phenols contentsinfo:eu-repo/semantics/publishedVersio

Machine Learning for Alzheimer’s Disease and Related Dementias

Copyright © The Author(s) 2023. Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.MM, LC, and SO research is supported by the Wellcome EPSRC Centre for Medical Engineering at King’s College London (WT 203148/Z/16/Z), EPSRC (EP/T022205/1), the Wellcome Trust (WT 215010/Z/18/Z), the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare, Medical Research Council (MRC), NIHR, Alzheimer’s Society, and the European Union. DMC is supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK (ARUK-PG2017-1946), the UCL/UCLH NIHR Biomedical Research Centre, and the UKRI Innovation Scholars: Data Science Training in Health and Bioscience (MR/V03863X/1). MB is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). MB’s work was also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society, and Alzheimer’s Research UK

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression. We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-βH1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples. We identified a total of 264 genes stably expressed in EndoC-βH1 cells and human islets following cytokines–or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells

Disentangling post-vaccination symptoms from early COVID-19

Background: Identifying and testing individuals likely to have SARS-CoV-2 is critical for infection control, including post-vaccination. Vaccination is a major public health strategy to reduce SARS-CoV-2 infection globally. Some individuals experience systemic symptoms post-vaccination, which overlap with COVID-19 symptoms. This study compared early post-vaccination symptoms in individuals who subsequently tested positive or negative for SARS-CoV-2, using data from the COVID Symptom Study (CSS) app. Methods: We conducted a prospective observational study in 1,072,313 UK CSS participants who were asymptomatic when vaccinated with Pfizer-BioNTech mRNA vaccine (BNT162b2) or Oxford-AstraZeneca adenovirus-vectored vaccine (ChAdOx1 nCoV-19) between 8 December 2020 and 17 May 2021, who subsequently reported symptoms within seven days (N=362,770) (other than local symptoms at injection site) and were tested for SARS-CoV-2 (N=14,842), aiming to differentiate vaccination side-effects per se from superimposed SARS-CoV-2 infection. The post-vaccination symptoms and SARS-CoV-2 test results were contemporaneously logged by participants. Demographic and clinical information (including comorbidities) were recorded. Symptom profiles in individuals testing positive were compared with a 1:1 matched population testing negative, including using machine learning and multiple models considering UK testing criteria. Findings: Differentiating post-vaccination side-effects alone from early COVID-19 was challenging, with a sensitivity in identification of individuals testing positive of 0.6 at best. Most of these individuals did not have fever, persistent cough, or anosmia/dysosmia, requisite symptoms for accessing UK testing; and many only had systemic symptoms commonly seen post-vaccination in individuals negative for SARS-CoV-2 (headache, myalgia, and fatigue). Interpretation: Post-vaccination symptoms per se cannot be differentiated from COVID-19 with clinical robustness, either using symptom profiles or machine-derived models. Individuals presenting with systemic symptoms post-vaccination should be tested for SARS-CoV-2 or quarantining, to prevent community spread. Funding: UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Chronic Disease Research Foundation, Zoe Limited

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study

Background Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. Methods In this large-scale, prospective, epidemiological surveillance study, we used prospective, observational, longitudinal, self-reported data from participants in the UK on 19 symptoms over 3 days after symptoms onset and COVID-19 PCR test results extracted from the COVID-19 Symptom Study mobile phone app. We divided the study population into a training set (those who reported symptoms between April 29, 2020, and Oct 15, 2020) and a test set (those who reported symptoms between Oct 16, 2020, and Nov 30, 2020), and used three models to analyse the selfreported symptoms: the UK’s National Health Service (NHS) algorithm, logistic regression, and the hierarchical Gaussian process model we designed to account for several important variables (eg, specific COVID-19 symptoms, comorbidities, and clinical information). Model performance to predict COVID-19 positivity was compared in terms of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the test set. For the hierarchical Gaussian process model, we also evaluated the relevance of symptoms in the early detection of COVID-19 in population subgroups stratified according to occupation, sex, age, and body-mass index. Findings The training set comprised 182 991 participants and the test set comprised 15 049 participants. When trained on 3 days of self-reported symptoms, the hierarchical Gaussian process model had a higher prediction AUC (0·80 [95% CI 0·80–0·81]) than did the logistic regression model (0·74 [0·74–0·75]) and the NHS algorithm (0·67 [0·67–0·67]). AUCs for all models increased with the number of days of self-reported symptoms, but were still high for the hierarchical Gaussian process model at day 1 (0·73 [95% CI 0·73–0·74]) and day 2 (0·79 [0·78–0·79]). At day 3, the hierarchical Gaussian process model also had a significantly higher sensitivity, but a non-statistically lower specificity, than did the two other models. The hierarchical Gaussian process model also identified different sets of relevant features to detect COVID-19 between younger and older subgroups, and between health-care workers and non-health-care workers. When used during different pandemic periods, the model was robust to changes in populations. Interpretation Early detection of SARS-CoV-2 infection is feasible with our model. Such early detection is crucial to contain the spread of COVID-19 and efficiently allocate medical resources. Funding ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer’s Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness

Influence of steps on the tilting and adsorption dynamics of ordered Pn films on vicinal Ag(111) surfaces

Here we present a structural study of pentacene (Pn) thin films on vicinal Ag(111) surfaces by He atom diffraction measurements and density functional theory (DFT) calculations supplemented with van der Waals (vdW) interactions. Our He atom diffraction results suggest initial adsorption at the step edges evidenced by initial slow specular reflection intensity decay rate as a function of Pn deposition time. In parallel with the experimental findings, our DFT+vdW calculations predict the step edges as the most stable adsorption site on the surface. An isolated molecule adsorbs as tilted on the step edge with a binding energy of 1.4 eV. In addition, a complete monolayer (ML) with pentacenes flat on the terraces and tilted only at the step edges is found to be more stable than one with all lying flat or tilted molecules, which in turn influences multilayers. Hence our results suggest that step edges can trap Pn molecules and act as nucleation sites for the growth of ordered thin films with a crystal structure similar to that of bulk Pn.Comment: 4 pages, 4 figures, 1 tabl