Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms

In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable

Molécules odorantes : olfaction, prise alimentaire, activité biologique

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Fonctionnalisation et genotoxicite dans la serie des nitroarenofurannes

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

A look back at 30 years of research into odours. In the magazine "New Flavourist", the new publication from the British Society of Flavourists

Suite à un podcast réalisé pour la British Society of Flavourists en mai 2024

Flavour Talks Podcast with Anne Tromelin

Scientifique invitée par la British Society of Flavourists dans l'émission "the Flavour Talks podcast".Listen to a new episode of the Flavour Talks podcast. Our guest is Anne Tromelin, a researcher at the INRA in Dijon, France. She studied pharmaceutical sciences at the University of Bourgundy, focusing on the biological activity of small organic molecules. She joined the INRA in Dijon, where she started working on QSAR studies, mainly on beta-lactoglobulin. Her career then evolved into studies on aroma perception using computational and chemoinformatics approaches. The podcast was held in French, an abridged version translated into English will appear in our magazine New Flavourist

Interaction between flavour compounds and beta-lactoglobulin: approach by NMR and 2D/3D-QSAR studies of ligands

Interactions between flavour compounds and beta-lactoglobulin (BLG) have been the subject of several studies, but there are no unanimous binding site explanations. In our laboratory, interactions between BLG, and two flavour compounds, beta-ionone and gamma-decalactone, were studied by 2D-NMR spectroscopy. It appears that several amino acids affected by binding of gamma-decalactone are buried in the central cavity, whereas binding of beta-ionone affects amino acids located in a groove near the outer surface of the protein. 2D/3D-QSAR studies were performed using QSAR+ module of Cerius2 and Catalyst. The QSAR equation provided by Cerius involves three molecular descriptors: AlogP98 and two topological connectivity indices (CHI-0 and CHI-1). This model takes into account hydrophobicity and molecular shape more than molecular volume. A relatively flat (e.g. a circle) and elongated (non-branched) shape appears to be able to increase the affinity for BLG. In this way, affinity appears to be strongly related to London dispersive forces. It adequately satisfies internal and external validation and allows a signicant, but not an accurate, prediction of binding of aroma compounds to BLG. The commercially available software Catalyst focuses the modelling on the molecular behaviour of a ligand interacting with a receptor from the point of view of the receptor, but using only information from the ligand. In this way, it appears to be appropriate to identify binding sub-sites on BLG. The 3D-QSAR models generation runs succeeded in providing signicant models which precisely estimated the affinities of sub-sets of compounds. A hydrogen bond acceptor and at least two hydrophobic features constitute the best models. Some model allows explaining the abnormal values of affinity constants of any compounds as -terpineol and highlights the importance of hydrogen bonding. Thus, Catalyst confirms the existence of at least two binding sites on the BLG

Application de l'approche QSAR/QSPR à la perception des arômes

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Application de l'approche QSAR/QSPR à la perception des arômes

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Cannabinoid ligands sorting out by a 3D-QSAR approach using catalyst/hypogen

National audienceUnderstanding how molecular structures are involved in recognition by a biological receptor is a decisive step in drug design, and could constitute an intricate problem because of existence of several binding sites, as is the case of GPCRs (1) that constitute the largest class of membrane receptors. In this context, identification of pharmacophores that should differentiate multiple binding modes is of particular interest. We have recently applied to ligands of a human olfactory receptor an original sorting-out procedure carried out using Catalyst/HypoGen software (Accelrys Ltd) (2). We aimed to validate this sorting out procedure using literature data, and in this way, we focused on CB1 agonists and antagonist. Indeed, CB1 ligands present several qualities: they have been extensively studied since several years, and it is now admitted that sites of cannabinoid agonists are distinguishable from binding sites of cannabinoid antagonists (3). Furthermore, CB1 receptor possess two distinct subsites for ligand binding (4,5). We used two training sets: the first one (P-23) constituted by 23 classical cannabinoids CB1 agonists (6), the second one (C-29) by 29 CB1 antagonists (7). Hypothesis generation was carried out separately on the two sets P-23 and C-29, and on the whole set (PC-53), testing several features associations of five features: Hydrogen Bond Acceptor (HBA), Hydrogen Bond Donor (HBD), Hydrophobic (HY), Hydrophobic Aliphatic (HYAL), Ring Aromatic (RA). The best significant hypothesis obtained for group P-23 was constituted by 1 HY, 3 HYAL and 1 RA (cost=160, correl=0.89, Config=15.5, fixed cost=66.7, null cost=500), whereas 1 HY, 2 HYAL and 1 HBD constituted the best significant hypothesis obtained for group C-29 (cost=186, correl=0.89, Config=15.9, fixed cost=80, null cost=554). Ten hypotheses were obtained starting from the merged group PC-53; two HBA and two HY constituted the best significant hypothesis (cost=569, correl=0.84, Config=14, fixed cost=128, null cost=1585). On the basis of compound's alignment, we identified two subsets that allowed to obtain significant models. The first one is constituted by 6 agonists (group P-6), and the second one by 5 antagonists (C-5). Activities estimation and addition of well estimated compounds respectively in the subsets P-6 and C-5 led to obtain two new groups: P-13 (best significant hypothesis: 2 HY, 2 HYAL, 1 RA, cost=47, correl=0.99, Config=15, fixed cost=44.5, null cost=237) and C-23 (best significant hypothesis: 2 HY, 2 HYAL, 1 HBD, cost=75, correl=0.98, Config=15, fixed cost=67, null cost=243). We have thus successfully separate agonists from antagonist; moreover our procedure allowed to improve the quality of both agonists and antagonists models, and to identify several outliers in agonist group that should be related to the existence of two distinct subsites for CB1 agonist binding (4,5)