Search

Your search keyword '"Meyer, Pablo"' showing total 23 results
Start Over Author "Meyer, Pablo" Publication Type Electronic Resources
23 results on '"Meyer, Pablo"'

1. Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

Author

Chari, Shruthi, Acharya, Prasant, Gruen, Daniel M., Zhang, Olivia, Eyigoz, Elif K., Ghalwash, Mohamed, Seneviratne, Oshani, Saiz, Fernando Suarez, Meyer, Pablo, Chakraborty, Prithwish, McGuinness, Deborah L., Chari, Shruthi, Acharya, Prasant, Gruen, Daniel M., Zhang, Olivia, Eyigoz, Elif K., Ghalwash, Mohamed, Seneviratne, Oshani, Saiz, Fernando Suarez, Meyer, Pablo, Chakraborty, Prithwish, and McGuinness, Deborah L.

Abstract

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease - a common type-2 diabetes comorbidity. All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.

Published
2023
Full Text
View/download PDF

2. Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI

Author

Stiglic, Gregor, Kopitar, Leon, Gosak, Lucija, Kocbek, Primoz, He, Zhe, Chakraborty, Prithwish, Meyer, Pablo, Bian, Jiang, Stiglic, Gregor, Kopitar, Leon, Gosak, Lucija, Kocbek, Primoz, He, Zhe, Chakraborty, Prithwish, Meyer, Pablo, and Bian, Jiang

Abstract

Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve the above-mentioned problem, we employed generative artificial intelligence techniques based on large-scale language models to summarize abstracts of scientific papers. Our objective is to investigate the potential of generative artificial intelligence in diminishing the cognitive load experienced by practitioners, thus exploring its ability to alleviate mental effort and burden. The study participants were provided with two use cases related to preventive care and behavior change, simulating a search for new scientific literature. The study included 113 university students from Slovenia and the United States randomized into three distinct study groups. The first group was assigned to the full abstracts. The second group was assigned to the short abstracts generated by AI. The third group had the option to select a full abstract in addition to the AI-generated short summary. Each use case study included ten retrieved abstracts. Our research demonstrates that the use of generative AI for literature review is efficient and effective. The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts. The results, however, also show significantly lower accuracy in extracted knowledge in cases where full abstract was not available. Such a disruptive technology could significantly reduce the time required for healthcare professionals to keep up with the most recent scientific literature; nevertheless, further developments are needed to help them comprehend the knowledge accurately., Comment: 5 pages, 5 figures

Published
2023

3. Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database

Author

Nyemba, Steve, Yan, Chao, Zhang, Ziqi, Rajmane, Amol, Meyer, Pablo, Chakraborty, Prithwish, Malin, Bradley, Nyemba, Steve, Yan, Chao, Zhang, Ziqi, Rajmane, Amol, Meyer, Pablo, Chakraborty, Prithwish, and Malin, Bradley

Abstract

Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML need to be portable if they are to be adopted on a broad scale. In this respect, models developed at one institution should be reusable at another. Yet there are numerous examples of portability failure, particularly due to naive application of ML models. Portability failure can lead to suboptimal care and medical errors, which ultimately could prevent the adoption of ML-based CDS in practice. One specific healthcare challenge that could benefit from enhanced portability is the prediction of 30-day readmission risk. Research to date has shown that deep learning models can be effective at modeling such risk. In this work, we investigate the practicality of model portability through a cross-site evaluation of readmission prediction models. To do so, we apply a recurrent neural network, augmented with self-attention and blended with expert features, to build readmission prediction models for two independent large scale claims datasets. We further present a novel transfer learning technique that adapts the well-known method of born-again network (BAN) training. Our experiments show that direct application of ML models trained at one institution and tested at another institution perform worse than models trained and tested at the same institution. We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data. Notably, this improvement is consistent across both sites and occurs after a single retraining, which illustrates the potential for a cheap and general model transfer mechanism of readmission risk prediction.

Published
2022

4. Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms.

Author

UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Gerkin, Richard C, Ohla, Kathrin, Veldhuizen, Maria G, Joseph, Paule V, Kelly, Christine E, Bakke, Alyssa J, Steele, Kimberley E, Farruggia, Michael C, Pellegrino, Robert, Pepino, Marta Y, Bouysset, Cédric, Soler, Graciela M, Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland W, Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet Hakan, Fjaeldstad, Alexander W, Lin, Cailu, Sandell, Mari A, Singh, Preet B, Brindha, V Evelyn, Olsson, Shannon B, Saraiva, Luis R, Ahuja, Gaurav, Alwashahi, Mohammed K, Bhutani, Surabhi, D'Errico, Anna, Fornazieri, Marco A, Golebiowski, Jérôme, Dar Hwang, Liang, Öztürk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine L, Faraji, Farhoud, Fischmeister, Florian Ph S, Heinbockel, Thomas, Hsieh, Julien W, Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas K, Philpott, Carl M, Pierron, Denis, Shields, Vonnie D C, Voznessenskaya, Vera V, Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, María Adelaida, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundström, Johan N, Macchi, Alberto, Meyer, Pablo, Moein, Shima T, Santamaría, Enrique, Sengupta, Debarka, Rohlfs Dominguez, Paloma, Yanik, Hüseyin, Hummel, Thomas, Hayes, John E, Reed, Danielle R, Niv, Masha Y, Munger, Steven D, Parma, Valentina, GCCR Group Author, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Gerkin, Richard C, Ohla, Kathrin, Veldhuizen, Maria G, Joseph, Paule V, Kelly, Christine E, Bakke, Alyssa J, Steele, Kimberley E, Farruggia, Michael C, Pellegrino, Robert, Pepino, Marta Y, Bouysset, Cédric, Soler, Graciela M, Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland W, Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet Hakan, Fjaeldstad, Alexander W, Lin, Cailu, Sandell, Mari A, Singh, Preet B, Brindha, V Evelyn, Olsson, Shannon B, Saraiva, Luis R, Ahuja, Gaurav, Alwashahi, Mohammed K, Bhutani, Surabhi, D'Errico, Anna, Fornazieri, Marco A, Golebiowski, Jérôme, Dar Hwang, Liang, Öztürk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine L, Faraji, Farhoud, Fischmeister, Florian Ph S, Heinbockel, Thomas, Hsieh, Julien W, Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas K, Philpott, Carl M, Pierron, Denis, Shields, Vonnie D C, Voznessenskaya, Vera V, Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, María Adelaida, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundström, Johan N, Macchi, Alberto, Meyer, Pablo, Moein, Shima T, Santamaría, Enrique, Sengupta, Debarka, Rohlfs Dominguez, Paloma, Yanik, Hüseyin, Hummel, Thomas, Hayes, John E, Reed, Danielle R, Niv, Masha Y, Munger, Steven D, Parma, Valentina, and GCCR Group Author

Abstract

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 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

Published
2021

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

Author

UCL - SSS/IONS - Institute of NeuroScience, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, UCL - SSS/IONS - Institute of NeuroScience, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, and Hayes, John E

Abstract

This is a correction notice for article bjaa041 (DOI: https://doi.org/10.1093/chemse/bjaa041), published 20 June 2020. An incorrect version of the caption to Figure 5 was mistakenly included in the published paper. An updated version is given below. Neither the data nor the paper’s conclusions were affected by this correction. The authors sincerely apologize for the error.

Published
2021

6. Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms

Author

Gerkin, Richard C., Ohla, Kathrin, Veldhuizen, Maria G., Joseph, Paule, Kelly, Christine E., Bakke, Alyssa J., Steele, Kimberley E., Farruggia, Michael C., Pellegrino, Robert, Pepino, Marta Y., Bouysset, Cedric, Soler, Graciela M., Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland W., Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet Hakan, Fjaeldstad, Alexander W., Lin, Cailu, Sandell, Mari A., Singh, Preet B., Brindha, V. Evelyn, Olsson, Shannon B., Saraiva, Luis R., Ahuja, Gaurav, Alwashahi, Mohammed K., Bhutani, Surabhi, D'Errico, Anna, Fornazieri, Marco A., Golebiowski, Jerome, Hwang, Liang Dar, Ozturk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine L., Faraji, Farhoud, Fischmeister, Florian Ph S., Heinbockel, Thomas, Hsieh, Julien W., Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas K., Philpott, Carl M., Pierron, Denis, Shields, Vonnie D. C., Voznessenskaya, Vera V., Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, Maria Adelaida, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundstrom, Johan N., Macchi, Alberto, Meyer, Pablo, Moein, Shima T., Santamaria, Enrique, Sengupta, Debarka, Dominguez, Paloma Rohlfs, Yanik, Huseyin, Hummel, Thomas, Hayes, John E., Reed, Danielle R., Niv, Masha Y., Munger, Steven D., Parma, Valentina, Gerkin, Richard C., Ohla, Kathrin, Veldhuizen, Maria G., Joseph, Paule, Kelly, Christine E., Bakke, Alyssa J., Steele, Kimberley E., Farruggia, Michael C., Pellegrino, Robert, Pepino, Marta Y., Bouysset, Cedric, Soler, Graciela M., Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland W., Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet Hakan, Fjaeldstad, Alexander W., Lin, Cailu, Sandell, Mari A., Singh, Preet B., Brindha, V. Evelyn, Olsson, Shannon B., Saraiva, Luis R., Ahuja, Gaurav, Alwashahi, Mohammed K., Bhutani, Surabhi, D'Errico, Anna, Fornazieri, Marco A., Golebiowski, Jerome, Hwang, Liang Dar, Ozturk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine L., Faraji, Farhoud, Fischmeister, Florian Ph S., Heinbockel, Thomas, Hsieh, Julien W., Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas K., Philpott, Carl M., Pierron, Denis, Shields, Vonnie D. C., Voznessenskaya, Vera V., Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, Maria Adelaida, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundstrom, Johan N., Macchi, Alberto, Meyer, Pablo, Moein, Shima T., Santamaria, Enrique, Sengupta, Debarka, Dominguez, Paloma Rohlfs, Yanik, Huseyin, Hummel, Thomas, Hayes, John E., Reed, Danielle R., Niv, Masha Y., Munger, Steven D., and Parma, Valentina

Abstract

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 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

Published
2021
Full Text
View/download PDF

7. Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms.

Author

Gerkin, Richard, Gerkin, Richard, Ohla, Kathrin, Veldhuizen, Maria, Joseph, Paule, Kelly, Christine, Bakke, Alyssa, Steele, Kimberley, Farruggia, Michael, Pellegrino, Robert, Pepino, Marta, Bouysset, Cédric, Soler, Graciela, Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland, Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet, Fjaeldstad, Alexander, Lin, Cailu, Sandell, Mari, Singh, Preet, Brindha, V, Olsson, Shannon, Saraiva, Luis, Ahuja, Gaurav, Alwashahi, Mohammed, Bhutani, Surabhi, DErrico, Anna, Fornazieri, Marco, Golebiowski, Jérôme, Dar Hwang, Liang, Öztürk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine, Faraji, Farhoud, Fischmeister, Florian, Heinbockel, Thomas, Hsieh, Julien, Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas, Philpott, Carl, Pierron, Denis, Shields, Vonnie, Voznessenskaya, Vera, Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, María, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundström, Johan, Macchi, Alberto, Meyer, Pablo, Moein, Shima, Santamaría, Enrique, Sengupta, Debarka, Rohlfs Dominguez, Paloma, Yanik, Hüseyin, Hummel, Thomas, Hayes, John, Reed, Danielle, Niv, Masha, Munger, Steven, Parma, Valentina, Gerkin, Richard, Gerkin, Richard, Ohla, Kathrin, Veldhuizen, Maria, Joseph, Paule, Kelly, Christine, Bakke, Alyssa, Steele, Kimberley, Farruggia, Michael, Pellegrino, Robert, Pepino, Marta, Bouysset, Cédric, Soler, Graciela, Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland, Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet, Fjaeldstad, Alexander, Lin, Cailu, Sandell, Mari, Singh, Preet, Brindha, V, Olsson, Shannon, Saraiva, Luis, Ahuja, Gaurav, Alwashahi, Mohammed, Bhutani, Surabhi, DErrico, Anna, Fornazieri, Marco, Golebiowski, Jérôme, Dar Hwang, Liang, Öztürk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine, Faraji, Farhoud, Fischmeister, Florian, Heinbockel, Thomas, Hsieh, Julien, Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas, Philpott, Carl, Pierron, Denis, Shields, Vonnie, Voznessenskaya, Vera, Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, María, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundström, Johan, Macchi, Alberto, Meyer, Pablo, Moein, Shima, Santamaría, Enrique, Sengupta, Debarka, Rohlfs Dominguez, Paloma, Yanik, Hüseyin, Hummel, Thomas, Hayes, John, Reed, Danielle, Niv, Masha, Munger, Steven, and Parma, Valentina

Abstract

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 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

Published
2021

8. Corrigendum to: More Than Smell—COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B., Gerkin, Richard C., Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W., Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, Katherine L., Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn V., Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N., Mucignat, Carla, Parker, Jane K., Van Den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S., Heinbockel, Thomas, Shields, Vonnie D.C., Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E.A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Welge-Lüssen, Antje, Freiherr, Jessica, De Groot, Jasper H.B., Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W., Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H., Voznessenskaya, Vera V., Chen, Jingguo, Sell, Elizabeth A., Walsh-Messinger, Julie, Archer, Nicholas S., Koyama, Sachiko, Deary, Vincent, Roberts, S.C., Yanlk, Hüseyin, Albayrak, Samet, Nováková, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T., Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K., Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Razzaghi-Asl, Sara, Justice, Jeb M., Restrepo, Diego, Reed, Danielle R., Hummel, Thomas, Munger, Steven D., Hayes, John E., Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B., Gerkin, Richard C., Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W., Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, Katherine L., Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn V., Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N., Mucignat, Carla, Parker, Jane K., Van Den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S., Heinbockel, Thomas, Shields, Vonnie D.C., Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E.A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Welge-Lüssen, Antje, Freiherr, Jessica, De Groot, Jasper H.B., Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W., Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H., Voznessenskaya, Vera V., Chen, Jingguo, Sell, Elizabeth A., Walsh-Messinger, Julie, Archer, Nicholas S., Koyama, Sachiko, Deary, Vincent, Roberts, S.C., Yanlk, Hüseyin, Albayrak, Samet, Nováková, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T., Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K., Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Razzaghi-Asl, Sara, Justice, Jeb M., Restrepo, Diego, Reed, Danielle R., Hummel, Thomas, Munger, Steven D., and Hayes, John E.

Abstract

This is a correction notice for article bjaa041 (DOI: https:// doi.org/10.1093/chemse/bjaa041), published 20 June 2020. An incorrect version of the caption to Figure 5 was mistakenly included in the published paper. An updated version is given below. Neither the data nor the paper's conclusions were affected by this correction. The authors sincerely apologize for the error. (A) Correlations between the 3 principal components with respect to changes in 3 chemosensory modalities (i.e., taste, smell, and chemesthesis). Shades of gray indicate positive correlation, whereas shades of red indicate negative correlations. White denotes no correlation. (B) Clusters of participants identified by k-means clustering. The scatterplot shows each participant's loading on dimension 1 (degree of smell and taste loss, PC1 on x-Axis) and dimension 2 (degree of chemesthesis loss, PC2 on y-Axis). Based on the centroid of each cluster, participants in cluster 1 (blue, N = 1767; top left) are generally characterized by significant smell, taste and chemesthesis loss. Participants in cluster 2 (orange, N = 1724; bottom center) are generally characterized by ratings that reflect smell/taste loss with preserved chemesthesis. Loadings for participants in cluster 3 (green, N = 548; right side) are generally characterized by reduced smell and taste loss, and preserved chemesthesis.

Published
2021

9. Disease Progression Modeling Workbench 360

Author

Suryanarayanan, Parthasarathy, Chakraborty, Prithwish, Madan, Piyush, Bore, Kibichii, Ogallo, William, Chandra, Rachita, Ghalwash, Mohamed, Buleje, Italo, Remy, Sekou, Mahatma, Shilpa, Meyer, Pablo, Hu, Jianying, Suryanarayanan, Parthasarathy, Chakraborty, Prithwish, Madan, Piyush, Bore, Kibichii, Ogallo, William, Chandra, Rachita, Ghalwash, Mohamed, Buleje, Italo, Remy, Sekou, Mahatma, Shilpa, Meyer, Pablo, and Hu, Jianying

Abstract

In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DPM360, when fully developed, will manage the entire modeling life cycle, from data analysis (e.g., cohort identification) to machine learning algorithm development and prototyping. DPM360 augments the advantages of data model standardization and tooling (OMOP-CDM, Athena, ATLAS) provided by the widely-adopted OHDSI initiative with a powerful machine learning training framework, and a mechanism for rapid prototyping through automatic deployment of models as containerized services to a cloud environment., Comment: Submitted to OHDSI Collaborator Showcase, 2021 (https://www.ohdsi.org/2021-collaborator-showcase)

Published
2021

10. More than just smell - COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Domínguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Pizio, Antonella Di, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Fialov, Jitka Trebická, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, KL, Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn V., Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N, Mucignat, Carla, Parker, Jane K., Brink, Mirjam van den, Schmuker, Michael, Fischmeister, Florian Ph.S, Heinbockel, Thomas, Shields, Vonnie D.C., Faraji, Farhoud, Santamaría, Enrique Enrique, Fredborg, William E.A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Abdulrahman, Olagunju, Welge-Lüssen, Antje, Dalton, Pamela, Freiherr, Jessica, Yan, Carol H., Groot, Jasper H. B. de, Voznessenskaya, Vera V., Klein, Hadar, Chen, Jingguo, Okamoto, Masako, Sell, Elizabeth A., Singh, Preet Bano, Walsh-Messinger, Julie, Archer, Nicholas S., Koyama, Sachiko, Deary, Vincent, Roberts, S. Craig, Yanik, Hüseyin, Albayrak, Samet, Novákov, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T., Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K., Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Asl, Sara Razzaghi, Justice, Jeb M., Restrepo, Diego, Reed, Danielle R., Hummel, Thomas, Munger, Steven, Hayes, John E, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Domínguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Pizio, Antonella Di, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Fialov, Jitka Trebická, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, KL, Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn V., Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N, Mucignat, Carla, Parker, Jane K., Brink, Mirjam van den, Schmuker, Michael, Fischmeister, Florian Ph.S, Heinbockel, Thomas, Shields, Vonnie D.C., Faraji, Farhoud, Santamaría, Enrique Enrique, Fredborg, William E.A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Abdulrahman, Olagunju, Welge-Lüssen, Antje, Dalton, Pamela, Freiherr, Jessica, Yan, Carol H., Groot, Jasper H. B. de, Voznessenskaya, Vera V., Klein, Hadar, Chen, Jingguo, Okamoto, Masako, Sell, Elizabeth A., Singh, Preet Bano, Walsh-Messinger, Julie, Archer, Nicholas S., Koyama, Sachiko, Deary, Vincent, Roberts, S. Craig, Yanik, Hüseyin, Albayrak, Samet, Novákov, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T., Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K., Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Asl, Sara Razzaghi, Justice, Jeb M., Restrepo, Diego, Reed, Danielle R., Hummel, Thomas, Munger, Steven, and Hayes, John E

Abstract

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, generally lacked quantitative measurements, and were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three 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, 8 other, ages 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±SD), 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 lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported financially with discretionary funds from the Pennsylvania State University (Penn State), including a gift from James and Helen Zallie g

Published
2020

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

Author

Parma, Valentina, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Parma, Valentina, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, and Hayes, John E

Abstract

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.

Published
2020

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

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B., Gerkin, Richard C., Dominguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W., Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, Katherine L., Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N., Mucignat, Carla, Parker, Jane K., van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S., Heinbockel, Thomas, Shields, Vonnie D. C., Faraji, Farhoud, Santamaria, Enrique, Fredborg, William E. A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H. B., Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W., Reed, Danielle R., Hummel, Thomas, Munger, Steven D., Hayes, John E., Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B., Gerkin, Richard C., Dominguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W., Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, Katherine L., Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N., Mucignat, Carla, Parker, Jane K., van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S., Heinbockel, Thomas, Shields, Vonnie D. C., Faraji, Farhoud, Santamaria, Enrique, Fredborg, William E. A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H. B., Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W., Reed, Danielle R., Hummel, Thomas, Munger, Steven D., and Hayes, John E.

Abstract

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.

Published
2020
Full Text
View/download PDF

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

Author

UCL - SSS/IONS - Institute of NeuroScience, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, UCL - SSS/IONS - Institute of NeuroScience, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, and Hayes, John E

Abstract

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.

Published
2020

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

Author

Parma, Valentina, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Parma, Valentina, Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, GCCR Group Author, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, and Hayes, John E

Abstract

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.

Published
2020

15. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Author

Tarca, Adi L., Lauria, Mario, Unger, Michael, Bilal, Erhan, Boue, Stephanie, Kumar Dey, Kushal, Hoeng, Julia, Koeppl, Heinz, Martin, Florian, Meyer, Pablo, Nandy, Preetam, Norel, Raquel, Peitsch, Manuel, Rice, Jeremy J., Romero, Roberto, Stolovitzky, Gustavo, Talikka, Marja, Xiang, Yang, Zechner, Christoph, Tarca, Adi L., Lauria, Mario, Unger, Michael, Bilal, Erhan, Boue, Stephanie, Kumar Dey, Kushal, Hoeng, Julia, Koeppl, Heinz, Martin, Florian, Meyer, Pablo, Nandy, Preetam, Norel, Raquel, Peitsch, Manuel, Rice, Jeremy J., Romero, Roberto, Stolovitzky, Gustavo, Talikka, Marja, Xiang, Yang, and Zechner, Christoph

Abstract

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/. Contact: gustav

Published
2017

16. Predicting human olfactory perception from chemical features of odor molecules.

Author

Keller, Andreas, Keller, Andreas, Gerkin, Richard C, Guan, Yuanfang, Dhurandhar, Amit, Turu, Gabor, Szalai, Bence, Mainland, Joel D, Ihara, Yusuke, Yu, Chung Wen, Wolfinger, Russ, Vens, Celine, Schietgat, Leander, De Grave, Kurt, Norel, Raquel, DREAM Olfaction Prediction Consortium, Stolovitzky, Gustavo, Cecchi, Guillermo A, Vosshall, Leslie B, Meyer, Pablo, Keller, Andreas, Keller, Andreas, Gerkin, Richard C, Guan, Yuanfang, Dhurandhar, Amit, Turu, Gabor, Szalai, Bence, Mainland, Joel D, Ihara, Yusuke, Yu, Chung Wen, Wolfinger, Russ, Vens, Celine, Schietgat, Leander, De Grave, Kurt, Norel, Raquel, DREAM Olfaction Prediction Consortium, Stolovitzky, Gustavo, Cecchi, Guillermo A, Vosshall, Leslie B, and Meyer, Pablo

Abstract

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

Published
2017

17. Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Author

Rhrissorrakrai, Kahn, Belcastro, Vincenzo, Bilal, Erhan, Norel, Raquel, Poussin, Carine, Mathis, Carole, Dulize, Rémi H. J., Ivanov, Nikolai V., Alexopoulos, Leonidas, Jeremy Rice, J., Peitsch, Manuel C., Stolovitzky, Gustavo, Meyer, Pablo, Hoeng, Julia, Rhrissorrakrai, Kahn, Belcastro, Vincenzo, Bilal, Erhan, Norel, Raquel, Poussin, Carine, Mathis, Carole, Dulize, Rémi H. J., Ivanov, Nikolai V., Alexopoulos, Leonidas, Jeremy Rice, J., Peitsch, Manuel C., Stolovitzky, Gustavo, Meyer, Pablo, and Hoeng, Julia

Abstract

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. Contact: pmeyerr@u

Published
2017

18. Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

Author

Massachusetts Institute of Technology. Department of Mathematics, Loh, Po-Ru, Tucker, George Jay, Lipson, Mark, Berger, Bonnie, Meyer, Pablo, Cokelaer, Thomas, Chandran, Deepak, Kim, Kyung H., Kreutz, Clemens, Raue, Andreas, Steiert, Bernhard, Timmer, Jens, Bilal, Erhan, DREAM 6&7 Parameter Estimation consortium, Sauro, Herbert M., Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Massachusetts Institute of Technology. Department of Mathematics, Loh, Po-Ru, Tucker, George Jay, Lipson, Mark, Berger, Bonnie, Meyer, Pablo, Cokelaer, Thomas, Chandran, Deepak, Kim, Kyung H., Kreutz, Clemens, Raue, Andreas, Steiert, Bernhard, Timmer, Jens, Bilal, Erhan, DREAM 6&7 Parameter Estimation consortium, Sauro, Herbert M., Stolovitzky, Gustavo, and Saez-Rodriguez, Julio

Abstract

Background: Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results: We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions: A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not complete, American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship, National Science Foundation (U.S.). Graduate Research Fellowship Program

Published
2014

19. Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

Author

Meyer, Pablo, Siwo, Geoffrey, Zeevi, Danny, Sharon, Eilon, Norel, Raquel, Segal, Eran, Stolovitzky, Gustavo, Rider, Andrew K., Tan, Asako, Pinapati, Richard S., Emrich, Scott, Chawla, Nitesh, Ferdig, Michael T., Tung, Yi-An, Chen, Yong-Syuan, Chen, Mei-Ju May, Chen, Chien-Yu, Knight, Jason M., Sahraeian, Sayed Mohammad Ebrahim, Esfahani, Mohammad Shahrokh, Dreos, Rene, Bucher, Philipp, Maier, Ezekiel, Saeys, Yvan, Szczurek, Ewa, Myšičková, Alena, Vingron, Martin, Klein, Holger, Kiełbasa, Szymon M., Knisley, Jeff, Bonnell, Jeff, Knisley, Debra, Kursa, Miron B., Rudnicki, Witold R., Bhattacharjee, Madhuchhanda, Sillanpää, Mikko J., Yeung, James, Meysman, Pieter, Rodríguez, Aminael Sánchez, Engelen, Kristof, Marchal, Kathleen, Huang, Yezhou, Mordelet, Fantine, Hartemink, Alexander, Pinello, Luca, Yuan, Guo-Cheng, Meyer, Pablo, Siwo, Geoffrey, Zeevi, Danny, Sharon, Eilon, Norel, Raquel, Segal, Eran, Stolovitzky, Gustavo, Rider, Andrew K., Tan, Asako, Pinapati, Richard S., Emrich, Scott, Chawla, Nitesh, Ferdig, Michael T., Tung, Yi-An, Chen, Yong-Syuan, Chen, Mei-Ju May, Chen, Chien-Yu, Knight, Jason M., Sahraeian, Sayed Mohammad Ebrahim, Esfahani, Mohammad Shahrokh, Dreos, Rene, Bucher, Philipp, Maier, Ezekiel, Saeys, Yvan, Szczurek, Ewa, Myšičková, Alena, Vingron, Martin, Klein, Holger, Kiełbasa, Szymon M., Knisley, Jeff, Bonnell, Jeff, Knisley, Debra, Kursa, Miron B., Rudnicki, Witold R., Bhattacharjee, Madhuchhanda, Sillanpää, Mikko J., Yeung, James, Meysman, Pieter, Rodríguez, Aminael Sánchez, Engelen, Kristof, Marchal, Kathleen, Huang, Yezhou, Mordelet, Fantine, Hartemink, Alexander, Pinello, Luca, and Yuan, Guo-Cheng

Abstract

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites.

Published
2013

20. Verification of systems biology research in the age of collaborative competition.

Author

Meyer, Pablo, Alexopoulos, Leonidas G, Bonk, Thomas, Califano, Andrea, Cho, Carolyn R., de la Fuente, Alberto, de Graaf, David, Hartemink, Alexander J., Hoeng, Julia, Ivanov, Nikolai V., Koeppl, Heinz, Linding, Rune, Marbach, Daniel, Norel, Raquel, Peitsch, Manuel C., Rice, J Jeremy, Royyuru, Ajay, Schacherer, Frank, Sprengel, Joerg, Stolle, Katrin, Vitkup, Dennis, Stolovitzky, Gustavo, Meyer, Pablo, Alexopoulos, Leonidas G, Bonk, Thomas, Califano, Andrea, Cho, Carolyn R., de la Fuente, Alberto, de Graaf, David, Hartemink, Alexander J., Hoeng, Julia, Ivanov, Nikolai V., Koeppl, Heinz, Linding, Rune, Marbach, Daniel, Norel, Raquel, Peitsch, Manuel C., Rice, J Jeremy, Royyuru, Ajay, Schacherer, Frank, Sprengel, Joerg, Stolle, Katrin, Vitkup, Dennis, and Stolovitzky, Gustavo

Published
2011

21. A crowd-sourcing approach for the construction of species-specific cell signaling networks

Author

Bilal, Erhan, Sakellaropoulos, Theodore, Participants, Challenge, Melas, Ioannis N., Messinis, Dimitris E., Belcastro, Vincenzo, Rhrissorrakrai, Kahn, Meyer, Pablo, Norel, Raquel, Iskandar, Anita, Blaese, Elise, Rice, John J., Peitsch, Manuel C., Hoeng, Julia, Stolovitzky, Gustavo, Alexopoulos, Leonidas G., Poussin, Carine, Bilal, Erhan, Sakellaropoulos, Theodore, Participants, Challenge, Melas, Ioannis N., Messinis, Dimitris E., Belcastro, Vincenzo, Rhrissorrakrai, Kahn, Meyer, Pablo, Norel, Raquel, Iskandar, Anita, Blaese, Elise, Rice, John J., Peitsch, Manuel C., Hoeng, Julia, Stolovitzky, Gustavo, Alexopoulos, Leonidas G., and Poussin, Carine

Abstract

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. Contact: ebilal@us.ibm.com or gustavo@us.ibm.com. Supplementary information: Supplementary data are available at Bioinformatics online

22. Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge

Author

Rhrissorrakrai, Kahn, Belcastro, Vincenzo, Bilal, Erhan, Norel, Raquel, Poussin, Carine, Mathis, Carole, Dulize, Rémi H. J., Ivanov, Nikolai V., Alexopoulos, Leonidas, Jeremy Rice, J., Peitsch, Manuel C., Stolovitzky, Gustavo, Meyer, Pablo, Hoeng, Julia, Rhrissorrakrai, Kahn, Belcastro, Vincenzo, Bilal, Erhan, Norel, Raquel, Poussin, Carine, Mathis, Carole, Dulize, Rémi H. J., Ivanov, Nikolai V., Alexopoulos, Leonidas, Jeremy Rice, J., Peitsch, Manuel C., Stolovitzky, Gustavo, Meyer, Pablo, and Hoeng, Julia

Abstract

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and ‘translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. Contact: pmeyerr@u

23. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Author

Tarca, Adi L., Lauria, Mario, Unger, Michael, Bilal, Erhan, Boue, Stephanie, Kumar Dey, Kushal, Hoeng, Julia, Koeppl, Heinz, Martin, Florian, Meyer, Pablo, Nandy, Preetam, Norel, Raquel, Peitsch, Manuel, Rice, Jeremy J., Romero, Roberto, Stolovitzky, Gustavo, Talikka, Marja, Xiang, Yang, Zechner, Christoph, Tarca, Adi L., Lauria, Mario, Unger, Michael, Bilal, Erhan, Boue, Stephanie, Kumar Dey, Kushal, Hoeng, Julia, Koeppl, Heinz, Martin, Florian, Meyer, Pablo, Nandy, Preetam, Norel, Raquel, Peitsch, Manuel, Rice, Jeremy J., Romero, Roberto, Stolovitzky, Gustavo, Talikka, Marja, Xiang, Yang, and Zechner, Christoph

Abstract

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/. Contact: gustav

Catalog

Books, media, physical & digital resources
See catalog results