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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.

Objectives: First, establishment and validation of a novel questionnaire documenting the burden of xerostomia and sialadenitis symptoms, including quality of life. Second, to compare two versions regarding the answering scale (proposed developed answers Q3 vs. 0-10 visual analogue scale Q10) of our newly developed questionnaire, in order to evaluate their comprehension by patients and their reproducibility in time.Study Design: The study is a systematic review regarding the evaluation of the existing questionnaire and a cohort study regarding the validation of our new MSGS questionnaire.Materials and Methods: A Multidisciplinary Salivary Gland Society (MSGS) questionnaire consisting of 20 questions and two scoring systems was developed to quantify symptoms of dry mouth and sialadenitis. Validation of the questionnaire was carried out on 199 patients with salivary pathologies (digestive, nasal, or age-related xerostomia, post radiation therapy, post radioiodine therapy, Sjögren's syndrome, IgG4 disease, recurrent juvenile parotitis, stones, and strictures) and a control group of 66 healthy volunteers. The coherence of the questionnaire's items, its reliability to distinguish patients from healthy volunteers, its comparison with unstimulated sialometry, and the time to fill both versions were assessed.Results: The novel MSGS questionnaire showed good internal coherence of the items, indicating its pertinence: the scale reliability coefficients amounted to a Cronbach's alpha of 0.92 for Q10 and 0.90 for Q3. The time to complete Q3 and Q10 amounted, respectively, to 5.23 min (AE2.3 min) and 5.65 min (AE2.64 min) for patients and to 3.94 min (AE3.94 min) and 3.75 min (AE2.11 min) for healthy volunteers. The difference between Q3 and Q10 was not significant.Conclusion: We present a novel self-administered questionnaire quantifying xerostomia and non-tumoral salivary gland pathologies. We recommend the use of the Q10 version, as its scale type is well known in the literature and it translation for international use will be more accurate.

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Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.