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AbstractAn accurate estimation of parasitism rates and diversity of parasitoids of crop insect pests is a prerequisite for exploring processes leading to efficient natural biocontrol. Traditional methods such as rearing have been often limited by taxonomic identification, insect mortality and intensive work, but the advent of high-throughput sequencing (HTS) techniques, such as DNA metabarcoding, is increasingly seen as a reliable and powerful alternative approach. Little has been done to explore the benefits of such an approach for estimating parasitism rates and parasitoid diversity in an agricultural context. In this study, we compared the composition of parasitoid species and parasitism rates between rearing and DNA metabarcoding of host eggs and larvae of the millet head miner, Heliocheilus albipunctella De Joannis (Lepidoptera, Noctuidae), collected from millet fields in Senegal. We first assessed the detection threshold for the main ten endoparasitoids, by sequencing PCR products obtained from artificial dilution gradients of the parasitoid DNAs in the host moth. We then assessed the potential of DNA metabarcoding for diagnosing parasitism rates in samples collected from the field. Under controlled conditions, our results showed that relatively small quantities of parasitoid DNA (0.07 ng) were successfully detected within an eight-fold larger quantity of host DNA. Parasitoid diversity and parasitism rate estimates were always higher for DNA metabarcoding than for host rearing. Furthermore, metabarcoding detected multi-parasitism, cryptic parasitoid species and differences in parasitism rates between two different sampling sites. Metabarcoding shows promise for gaining a clearer understanding of the importance and complexity of host-parasitoid interactions in agro-ecosystems, with a view to improving pest biocontrol strategies.

International audience; During the early spring of 2020, as the COVID-19 (coronavirus disease 2019) pandemic spread across the globe like a viral tsunami, many countries urgently implemented unprecedented mitigation measures to slow it down, following the example of China, where the disease first emerged at the end of 2019. As a result, more than 3 billion unprepared people worldwide had to cope with living under stringent lockdown measures for weeks or months, depending on the country. Together with the deaths of hundreds of thousands of people from this disease, a frightening emotional shock has touched populations everywhere around the planet. Millions have lost their jobs, others have had their personal lives changed drastically; the elderly, for example, have sometimes been isolated from any visits. To our knowledge, no guidelines to prevent psychological trauma have been proposed, even for the most vulnerable individuals. Nonetheless, before this crisis began, several experts had warned about the potential impact of such measures on the populations’ mental health, based on previous experiences of quarantine, as summarised in a review published, as this pandemic reached Europe and before any lockdowns.1 This warning was confirmed a few days later by the publication of a nationwide study conducted in China reporting that the quarantine triggered a wide variety of psychological disorders.2 In France, one of the hardest hit countries in Europe, a nationwide lockdown started on 17 March and lasted till 11 May. Public health authorities made daily recommendations on how to prevent COVID-19 infection with almost no specific advice on psychological prevention.

International audience

La nécessité d’avoir des indicateurs de santé publique fiable est primordial pour avoir des réponses adaptées au niveau des moyens en particulier dans le contexte actuel de pandémie à COVID-19. En France, la régulation médicale permet d’obtenir une quantité de données importantes sur les appels passés. Nous avons obtenu grâce au « Natural langage processing » les données de 2010 à 2020 de la régulation au SMUR 33 concernant les symptômes grippaux de tous les patients soit plus de 2 millions textes libres, avec une catégorisation selon l’âge. Nous avons pris des patients de plus de 75 ans, mais également la catégorie des 65-75 ans. Les résultats montrent une augmentation globale du nombre d’appels, toutes causes confondues et pour syndrome grippal. Avec des catégories d’âge ayant des majorations différentes : les plus de 65 ans ayant le plus fort taux d’augmentation du nombre d’appel total et pour syndrome grippal. De plus, l’année 2020 avec l’émergence de la COVID 19 trouve une augmentation extrême des appels pour syndromes grippaux sur une courte période avec cependant une proportion moins importante des appels passés par les plus de 75 ans. L’utilisation de l’intelligence artificielle avec traitement naturel de langage des contenus des dossiers de régulation médicale permet une surveillance épidémiologique efficace et permet dans des situations sanitaires exceptionnelles d’avoir un outil supplémentaire efficient et accessible. The need for reliable public health indicators is crucial for appropriate resource responses, particularly in the current context of the COVID-19 pandemic. In France, medical regulation offers a significant amount of data on the calls made. Thanks to "Natural language processing", we have obtained data from 2010 to 2020 from SMUR 33 regulation concerning the flu symptoms of all patients, i.e. more than 2 million free texts, categorized according to age. We took patients over 75 years of age, but also the category 65-75 years of age. The results show an overall increase in the number of calls, all causes combined and for flu-like symptoms. With different age categories having different increases: the over 65s having the highest rate of increase in the total number of calls and for flu syndrome. Moreover, the year 2020 with the emergence of COVID 19 finds an extreme increase in the number of calls for flu syndromes over a short period of time with, however, a lower proportion of calls made by those over 75 years of age. The use of artificial intelligence with natural language processing of the contents of medical regulation files enables effective epidemiological surveillance and, in exceptional health situations, provides an additional efficient and accessible tool.

The recent emergence of the SARS-CoV-2 in China has raised the spectre of a novel, potentially catastrophic pandemic in both scientific and lay communities throughout the world. In this particular context, people have been accused of being excessively pessimistic regarding the future consequences of this emerging health threat. However, consistent with previous research in social psychology, a large survey conducted in Europe in the early stage of the COVID-19 epidemic shows that the majority of respondents was actually overly optimistic about the risk of infection. https://psyarxiv.com/364qj/

BackgroundA novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. ObjectiveThe aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). MethodsWe explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. ResultsIn this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. ConclusionsIn our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

The high neutrophil to lymphocyte ratio observed in critically ill patients with COVID-19 is associated with excessive levels of reactive oxygen species (ROS), which promote a cascade of biological events that drive pathological host responses. ROS induce tissue damage, thrombosis and red blood cell dysfunction, which contribute to COVID-19 disease severity. We suggest that free radical scavengers could be beneficial for the most vulnerable patients. In this Comment article, Becker and colleagues consider how the excessive release of reactive oxygen species by neutrophils may perpetuate red blood cell dysfunction, thrombosis and tissue damage in severe cases of COVID-19.