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.

Importance Sudden smell loss is a specific early symptom of COVID-19, with an estimated prevalence of ∼40% to 75%. Smell impairment affects physical and mental health, and dietary behavior. Thus, it is critical to understand the rate and time course of smell recovery. Objective To characterize smell function and recovery up to 11 months post COVID-19 infection. Settings, Participants This longitudinal survey of individuals suffering COVID-19-related smell loss assessed disease symptoms and gustatory and olfactory function. Participants (n=12,313) who completed an initial respiratory symptoms, chemosensory function, and COVID-19 diagnosis survey (S1) between April and September 2020 and completed a follow-up survey (S2) between September 2020 and February 2021; 27.5% participants responded (n=3,386), with 1,468 being diagnosed with COVID-19 and suffering co-occurring smell and taste loss at the beginning of their illness. Main Outcomes & Measures Primary outcomes are ratings of smell and taste function on a visual analog scale, and self-report of parosmia (smell distortions) and phantosmia (unexplained smells). Secondary outcomes include a checklist of other COVID-19 symptoms. Results On follow-up (median time since COVID-19 onset ∼200 days), ∼60% of women and ∼48% of men reported less than 80% of their pre-illness smell ability. Taste typically recovered faster than smell, and taste loss rarely persisted if smell recovered. Prevalence of parosmia and phantosmia was ∼10% of participants in S1 and increased substantially in S2: ∼47% for parosmia and ∼25% for phantosmia. Persistent smell impairment was associated with more symptoms overall, suggesting it may be a key marker of long-COVID. During COVID-19 illness, the ability to smell was slightly lower among those who did not recover their pre-illness ability to smell at S2. Conclusions and Relevance While smell loss improves for many individuals who lost it due to COVID-19, the prevalence of parosmia and phantosmia increases substantially over time. Olfactory dysfunction is also associated with wider COVID-19 symptoms and may persist for many months after COVID-19 onset. Taste loss in the absence of smell loss is rare. Persistent qualitative smell symptoms are emerging as common long-term sequelae; more research into treatment options is strongly warranted given that conservative estimates suggest millions of individuals may experience parosmia following COVID-19. Healthcare providers worldwide need to be prepared to treat post COVID-19 secondary effects on physical and mental health. Trial registration This project was pre-registered at OSF: https://osf.io/3e6zc. Question What are the characteristics of smell and taste recovery of COVID-19 patients? Findings In this preregistered observational study of 1,468 participants, smell loss is associated with a higher number of COVID-19 symptoms, and may persist for at least 11 months following disease onset. While a majority of participants report quantitative improvement in their ability to smell, the prevalence of parosmia and phantosmia increases substantially at follow-up. Taste recovers faster than smell, suggesting taste and smell recover separately and can be distinguished by the respondents. Meaning Olfactory dysfunction appears to be a component of long-COVID, with parosmia as a prominent symptom in almost half of those with smell loss. More research into treatment is needed, especially given that olfactory dysfunction is associated with depression and loss of appetite. Health professionals should be aware of these common and long lasting effects.

Background: COVID-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. Methods: This 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. Results: 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 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. Conclusions: As 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.

Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis for many cardiovascular system diseases. Therefore, accurate analyses of ECG signals are very important for correct diagnosis. In this study, an ECG analysis toolbox together with a user-friendly graphical user interface, which contains the all ECG analysis steps between the recording unit and the statistical investigation, is developed. Furthermore, a new feature calculation methodology is proposed for ECG analysis, which carries distinct information than amplitudes and durations of ECG main waves and can be used in artificial intelligence studies. Developed toolbox is tested using both Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia ECG Database and an experimentally collected dataset for performance evaluation. The results show that ECG analysis toolbox presented in this study increases the accuracy and reliability of the ECG main wave detection analysis, highly fasten the process duration compared to manual ones and the new feature set can be used as a new parameter for decision support systems about ECG based on artificial intelligence.

Considering its importance for vital activities, water and particularly drinking water should be clean and should not contain disease-causing bacteria. One of the pathogenic bacteria found in water is the bacterium Escherichia coli (E. coli). In the commonly used method for the detection of E. coli bacteria, the bacteria samples distilled from the water sample are seeded in endo agar medium and the change in the color of the medium as a result of the metabolic activities of the bacteria is examined with the naked eye. This color change can be seen with the human eye in approximately 22 ± 2 hours. In this study, a new bacteria detection scheme is proposed – using deep learning to detect E. coli bacteria both in shorter time and in practical way. The proposed technique is tested with experimentally collected data. Results show that the detection of bacteria can be done automatically within 6-10 hours with the proposed method.

Electrocardiography (ECG) signals and the information obtained from the analysis of these signals constitute the main source of information in determination of numerous cardiovascular system disease as well as in utility analysis of methods to be used for treatment of these diseases. In this study, ECG signals measured from living being are analyzed in computer environment in two parts namely filtering and feature extraction. In filtering phase, low frequency fluctuation on the baseline of the signal are straightened first using wavelet transform based filtering and then electric network noise are removed by designing a low pass butterworth filter. In feature extraction phase, timings of ECG characteristic waves (P, QRS complex, T) are determined using wavelet decomposition method. Obtained results show %95 or over success in determination of P, QRS and T timings. Measured ECG data used in this study are provided from Mersin University Medicine Faculty Biophysics Department and MIT physio database (www.phsionet.org).