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- Gerkin, RC, et al.
(författare)
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The best COVID-19 predictor is recent smell loss: a cross-sectional study
- 2020
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.MethodsThis preregistered, cross-sectional study used 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 singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.ResultsBoth 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 single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.ConclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
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| 2. |
- Dias, SB, et al.
(författare)
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Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease
- 2021
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Ingår i: Frontiers in psychology. - : Frontiers Media SA. - 1664-1078. ; 11, s. 612835-
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Tidskriftsartikel (refereegranskat)abstract
- Human-Computer Interaction (HCI) and games set a new domain in understanding people’s motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people’s health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson’s Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients’ quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.
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| 4. |
- Konstantinidis, D, et al.
(författare)
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Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos
- 2020
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Ingår i: Nutrients. - : MDPI AG. - 2072-6643. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel “Rapid Automatic Bite Detection” (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen’s kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
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| 5. |
- Murray, Alison E., et al.
(författare)
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Roadmap for naming uncultivated Archaea and Bacteria
- 2020
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Ingår i: Nature Microbiology. - : NATURE PUBLISHING GROUP. - 2058-5276. ; 5:8, s. 987-994
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Tidskriftsartikel (refereegranskat)abstract
- The assembly of single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) has led to a surge in genome-based discoveries of members affiliated with Archaea and Bacteria, bringing with it a need to develop guidelines for nomenclature of uncultivated microorganisms. The International Code of Nomenclature of Prokaryotes (ICNP) only recognizes cultures as 'type material', thereby preventing the naming of uncultivated organisms. In this Consensus Statement, we propose two potential paths to solve this nomenclatural conundrum. One option is the adoption of previously proposed modifications to the ICNP to recognize DNA sequences as acceptable type material; the other option creates a nomenclatural code for uncultivated Archaea and Bacteria that could eventually be merged with the ICNP in the future. Regardless of the path taken, we believe that action is needed now within the scientific community to develop consistent rules for nomenclature of uncultivated taxa in order to provide clarity and stability, and to effectively communicate microbial diversity. In this Consensus Statement, the authors discuss the issue of naming uncultivated prokaryotic microorganisms, which currently do not have a formal nomenclature system due to a lack of type material or cultured representatives, and propose two recommendations including the recognition of DNA sequences as type material.
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| 6. |
- Oleszkiewicz, A., et al.
(författare)
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Hedonic perception of odors in children aged 5–8 years is similar across 18 countries : Preliminary data
- 2022
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Ingår i: International Journal of Pediatric Otorhinolaryngology. - : Elsevier BV. - 0165-5876 .- 1872-8464. ; 157
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Olfactory preference emerges very early in life, and the sense of smell in children rapidly develops until the second decade of life. It is still unclear whether hedonic perception of odors is shared in children inhabiting different regions of the globe.Methods: Five-hundred ten healthy children (N = 510; ngirls = 256; nboys = 254) aged from 5 to 8 years from 18 countries rated the pleasantness of 17 odors.Results: The hedonic perception of odors in children aged between 5 and 8 years was rather consistent across 18 countries and mainly driven by the qualities of an odor and the overall ability of children to label odorants.Conclusion: Conclusions from this study, being a secondary analysis, are limited to the presented set of odors that were initially selected for the development of U-Sniff test and present null findings for the cross-cultural variability in hedonic perception of odors across 18 countries. These two major issues should be addressed in the future to either contradict or replicate the results presented herewith. This research lays fundament for posing further research questions about the developmental aspects of hedonic perception of odors and opens a new door for investigating cross-cultural differences in chemosensory perception of children.
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