Using absolutist word frequency from online searches to measure population mental health dynamics.

The assessment of population mental health relies on survey data from representative samples, which come with considerable costs. Drawing on research which established that absolutist words (e.g. never) are semantic markers for depression, we propose a new measure of population mental health based on the frequency of absolutist words in online search data (absolute thinking index; ATI). Our aims were to first validate the ATI, and to use it to model public mental health dynamics in France and the UK during the current COVID-19 pandemic. To do so, we extracted time series for a validated dictionary of 19 absolutist words, from which the ATI was computed (weekly averages, 2019-2020, n = 208) using Google Trends. We then tested the relationship between ATI and longitudinal survey data of population mental health in the UK (n = 36,520) and France (n = 32,000). After assessing the relationship between ATI and survey measures of depression and anxiety in both populations, and dynamic similarities between ATI and survey measures (France), we tested the ATI's construct validity by showing how it was affected by the pandemic and how it can be predicted by COVID-19-related indicators. A final step consisted in replicating ATI's construct validity tests in Japan, thereby providing evidence for the ATI's cross-cultural generalizability. ATI was linked with survey depression scores in the UK, r = 0.68, 95%CI[0.34,0.86], ß = 0.23, 95%CI[0.09,0.37] in France and displayed similar trends. We finally assessed the pandemic's impact on ATI using Bayesian structural time-series models. These revealed that the pandemic increased ATI by 3.2%, 95%CI[2.1,4.2] in France and 3.7%, 95%CI[2.9,4.4] in the UK. Mixed-effects models showed that ATI was related to COVID-19 new deaths in both countries ß = 0.14, 95%CI[0.14,0.21]. These patterns were replicated in Japan, with a pandemic impact of 4.9%, 95%CI[3.1,6.7] and an influence of COVID-19 death of ß = 0.90, 95%CI[0.36,1.44]. Our results demonstrate the validity of the ATI as a measure of population mental health (depression) in France, the UK and to some extent in Japan. We propose that researchers use it as cost-effective public mental health "thermometer" for applied and research purposes.

Smart investment of virus RNA testing resources to enhance Covid-19 mitigation.

A variety of mitigation strategies have been employed against the Covid-19 pandemic. Social distancing is still one of the main methods to reduce spread, but it entails a high toll on personal freedom and economic life. Alternative mitigation strategies that do not come with the same problems but are effective at preventing disease spread are therefore needed. Repetitive mass-testing using PCR assays for viral RNA has been suggested, but as a stand-alone strategy this would be prohibitively resource intensive. Here, we suggest a strategy that aims at targeting the limited resources available for viral RNA testing to subgroups that are more likely than the average population to yield a positive test result. Importantly, these pre-selected subgroups include symptom-free people. By testing everyone in these subgroups, in addition to symptomatic cases, large fractions of pre- and asymptomatic people can be identified, which is only possible by testing-based mitigation. We call this strategy smart testing (ST). In principle, pre-selected subgroups can be found in different ways, but for the purpose of this study we analyze a pre-selection procedure based on cheap and fast virus antigen tests. We quantify the potential reduction of the epidemic reproduction number by such a two-stage ST strategy. In addition to a scenario where such a strategy is available to the whole population, we analyze local applications, e.g. in a country, company, or school, where the tested subgroups are also in exchange with the untested population. Our results suggest that a two-stage ST strategy can be effective to curb pandemic spread, at costs that are clearly outweighed by the economic benefit. It is technically and logistically feasible to employ such a strategy, and our model predicts that it is even effective when applied only within local groups. We therefore recommend adding two-stage ST to the portfolio of available mitigation strategies, which allow easing social distancing measures without compromising public health.

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Reframing aging during COVID-19: Familial role-based framing of older adults linked to decreased ageism.

BACKGROUND: Contributions of older adults amid the COVID-19 pandemic have been eclipsed by discourse positioning them as an at-risk population. We assess whether age-based framing (e.g., senior citizen) is associated with more negative stereotyping in the media compared to familial role-based framing (e.g., grandparent) across 8 months, from a baseline period (October 2019-December 2019) to the onset of the pandemic (January 2020-May 2020). METHODS: Leveraging a 12-billion-word news media database-with over 30 million news articles from over 7000 websites-we identified the most common synonyms for age-based framing (e.g., senior citizen) and familial role-based framing (e.g., grandparent). For each framing category, we compiled the most frequently used descriptors every month, amassing 488,907 descriptors in total. All descriptors were rated from 1 (very negative) to 5 (very positive) to determine a Cumulative Aging Narrative Score (CANS) for age-based and familial role-based framing. RESULTS: Age-based framing of older adults increased negative stereotyping in the media by seven times compared to familial role-based framing during COVID-19. The percentage of positive topics for age-based framing was significantly lower during COVID-19 (35%) than before (61%). Conversely, the percentage of positive topics for familial role-based framing was higher during the pandemic (91%) than before (70%). CONCLUSION: This is one of the first empirical studies on whether framing older adults based on age or role is linked to more negative stereotypes during COVID-19. We argue for a more role-centered approach in framing older adults so that their contributions are acknowledged and valued by society.

Recognizing species as a new focus of virus research.

Species taxa are the units of taxonomy most suited to measure virus diversity, and they account for more than 70% of all virus taxa. Yet, as evidenced by the content of GenBank entries and illustrated by the recent literature on SARS-CoV-2, they are the most neglected taxa of virus research. To correct this disparity, we propose to make species taxa a first choice for communicating virus taxonomy in publications concerning viruses. We see it as a key step toward promoting research on diverse viruses, including pathogens, at this fundamental level of biology.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning.

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Covid-19 and ageing: four alternative conceptual frameworks.

Ageing is one of the main risk factors for Covid-19. In this paper, we delineate four alternative conceptualisations of ageing, each of which determines different understandings of its causal role to the susceptibility to Covid-19 as well as to the severity of its symptoms and adverse health outcomes.

Caffeoylquinic acids: chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity.

Caffeoylquinic acids (CQAs) are specialized plant metabolites we encounter in our daily life. Humans consume CQAs in mg-to-gram quantities through dietary consumption of plant products. CQAs are considered beneficial for human health, mainly due to their anti-inflammatory and antioxidant properties. Recently, new biosynthetic pathways via a peroxidase-type p-coumaric acid 3-hydroxylase enzyme were discovered. More recently, a new GDSL lipase-like enzyme able to transform monoCQAs into diCQA was identified in Ipomoea batatas. CQAs were recently linked to memory improvement; they seem to be strong indirect antioxidants via Nrf2 activation. However, there is a prevalent confusion in the designation and nomenclature of different CQA isomers. Such inconsistencies are critical and complicate bioactivity assessment since different isomers differ in bioactivity and potency. A detailed explanation regarding the origin of such confusion is provided, and a recommendation to unify nomenclature is suggested. Furthermore, for studies on CQA bioactivity, plant-based laboratory animal diets contain CQAs, which makes it difficult to include proper control groups for comparison. Therefore, a synthetic diet free of CQAs is advised to avoid interferences since some CQAs may produce bioactivity even at nanomolar levels. Biotransformation of CQAs by gut microbiota, the discovery of new enzymatic biosynthetic and metabolic pathways, dietary assessment, and assessment of biological properties with potential for drug development are areas of active, ongoing research. This review is focused on the chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity recently reported for mono-, di-, tri-, and tetraCQAs.

Editorial: Revised World Health Organization (WHO) Terminology for Variants of Concern and Variants of Interest of SARS-CoV-2.

The global pandemic of coronavirus disease 2019 (COVID-19) has identified thousands of genome sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 31st May 2021, the Virus Evolution Working Group of the World Health Organization (WHO) announced its recommendations for revised naming of SARS-CoV-2 variants of concern (VOCs) and variants of interest (VOIs). This new nomenclature system may improve infection monitoring, infection control, and sharing of research data on viral genomics and epidemiology. This Editorial aims to present an update on the current revised WHO terminology for the genomic VOCs and VOIs of SARS-CoV-2.