Text-based predictions of COVID-19 diagnosis from self-reported chemosensory descriptions.

BACKGROUND: There is a prevailing view that humans' capacity to use language to characterize sensations like odors or tastes is poor, providing an unreliable source of information. METHODS: Here, we developed a machine learning method based on Natural Language Processing (NLP) using Large Language Models (LLM) to predict COVID-19 diagnosis solely based on text descriptions of acute changes in chemosensation, i.e., smell, taste and chemesthesis, caused by the disease. The dataset of more than 1500 subjects was obtained from survey responses early in the COVID-19 pandemic, in Spring 2020. RESULTS: When predicting COVID-19 diagnosis, our NLP model performs comparably (AUC ROC ~ 0.65) to models based on self-reported changes in function collected via quantitative rating scales. Further, our NLP model could attribute importance of words when performing the prediction; sentiment and descriptive words such as "smell", "taste", "sense", had strong contributions to the predictions. In addition, adjectives describing specific tastes or smells such as "salty", "sweet", "spicy", and "sour" also contributed considerably to predictions. CONCLUSIONS: Our results show that the description of perceptual symptoms caused by a viral infection can be used to fine-tune an LLM model to correctly predict and interpret the diagnostic status of a subject. In the future, similar models may have utility for patient verbatims from online health portals or electronic health records.

Early in the COVID-19 pandemic, people who were infected with SARS-CoV-2 reported changes in smell and taste. To better study these symptoms of SARS-CoV-2 infections and potentially use them to identify infected patients, a survey was undertaken in various countries asking people about their COVID-19 symptoms. One part of the questionnaire asked people to describe the changes in smell and taste they were experiencing. We developed a computational program that could use these responses to correctly distinguish people that had tested positive for SARS-CoV-2 infection from people without SARS-CoV-2 infection. This approach could allow rapid identification of people infected with SARS-CoV-2 from descriptions of their sensory symptoms and be adapted to identify people infected with other viruses in the future.

A measure of smell enables the creation of olfactory metamers.

Wavelength is a physical measure of light, and the intricate understanding of its link to perceived colour enables the creation of perceptual entities such as metamers-non-overlapping spectral compositions that generate identical colour percepts1. By contrast, scientists have been unable to develop a physical measure linked to perceived smell, even one that merely reflects the extent of perceptual similarity between odorants2. Here, to generate such a measure, we collected perceptual similarity estimates of 49,788 pairwise odorants from 199 participants who smelled 242 different multicomponent odorants and used these data to refine a predictive model that links odorant structure to odorant perception3. The resulting measure combines 21 physicochemical features of the odorants into a single number-expressed in radians-that accurately predicts the extent of perceptual similarity between multicomponent odorant pairs. To assess the usefulness of this measure, we investigated whether we could use it to create olfactory metamers. To this end, we first identified a cut-off in the measure: pairs of multicomponent odorants that were within 0.05 radians of each other or less were very difficult to discriminate. Using this cut-off, we were able to design olfactory metamers-pairs of non-overlapping molecular compositions that generated identical odour percepts. The accurate predictions of perceptual similarity, and the ensuing creation of olfactory metamers, suggest that we have obtained a valid olfactory measure, one that may enable the digitization of smell.

More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis.

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± standard deviation), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis. The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

From virgin education to real education.

The now famous virgin headspace experiment from late Braja D. Mookherjee and Subha Patel, and its use in the creation of scent 'Virgin No 1' by Christophe Laudamiel and Christoph Hornetz for the novel by Patrick Süskind, and the movie by Tom Tykwer 'Perfume--The Story of a Murderer' (Constantin Film-Thierry Mugler Parfums-IFF, 2006) is discussed. Another fragrance from the luxury coffret, 'Salon Rouge', is described as well, illustrating how molecules and natural ingredients can be utilized, not only to create innovative or artistic fragrance compositions, but also to provide fundamental consumer education, improving the public image both of Chemistry and Perfumery.