Data-science based analysis of perceptual spaces of odors in olfactory loss

An olfactory perceptual fingerprint in people with olfactory dysfunction due to COVID-19

The sense of smell is based on sensory detection of the molecule(s), which is then further perceptually interpreted. A possible measure of olfactory perception is an odor-independent olfactory perceptual fingerprint (OPF) defined by Snitz et al. We aimed to investigate whether OPF can distinguish patients with olfactory dysfunction (OD) due to coronavirus disease (COVID-19) from controls and which perceptual descriptors are important for that separation. Our study included 99 healthy controls and 41 patients. They rated 10 odors using 8 descriptors such as "pleasant," "intense," "familiar," "warm," "cold," "irritating," "edible," and "disgusting." An unsupervised machine learning method, hierarchical cluster analysis, showed that OPF can distinguish patients from controls with an accuracy of 83%, a sensitivity of 51%, and a specificity of 96%. Furthermore, a supervised machine learning method, random forest classifier, showed that OPF can distinguish patients and controls in the testing dataset with an accuracy of 86%, a sensitivity of 64%, and a specificity of 96%. Principal component analysis and random forest classifier showed that familiarity and intensity were the key qualities to explain the variance of the data. In conclusion, people with COVID-19-related OD have a fundamentally different olfactory perception.

Perceptual odor qualities predict successful odor identification in old age

Odor identification is a common assessment of olfaction, and it is affected in a large number of diseases. Identification abilities decline with age, but little is known about whether there are perceptual odor features that can be used to predict identification. Here, we analyzed data from a large, population-based sample of 2,479 adults, aged 60 years or above, from the Swedish National study on Aging and Care in Kungsholmen. Participants performed both free and cued odor identification tests. In a separate experiment, we assessed perceived pleasantness, familiarity, intensity, and edibility of all odors in the first sample, and examined how odor identification performance is associated with these variables. The analysis showed that high-intensity odors are easier to identify than low-intensity odors overall, but also that they are more susceptible to the negative repercussions of old age. This result indicates that sensory decline is a major aspect of age-dependent odor identification impairment, and suggests a framework where identification likelihood is proportional to the perceived intensity of the odor. Additional analyses further showed that high-performing individuals can discriminate target odors from distractors along the pleasantness and edibility dimensions and that unpleasant and inedible odors show smaller age-related differences in identification. Altogether, these results may guide further development and optimization of brief and efficient odor identification tests as well as influence the design of odorous products targeted toward older consumers.

Antioxidant Activity of Pharmaceuticals: Predictive QSAR Modeling for Potential Therapeutic Strategy

Since oxidative stress has been linked to several pathological conditions and diseases, drugs with additional antioxidant activity can be beneficial in the treatment of these diseases. Therefore, this study takes a new look at the antioxidant activity of frequently prescribed drugs using the HPLC-DPPH method. The antioxidative activity expressed as the TEAC value of 82 drugs was successfully determined and is discussed in this work. Using the obtained values, the QSAR model was developed to predict the TEAC based on the selected molecular descriptors. The results of QSAR modeling showed that four- and seven-variable models had the best potential for TEAC prediction. Looking at the statistical parameters of each model, the four-variable model was superior to seven-variable. The final model showed good predicting power (r = 0.927) considering the selected descriptors, implying that it can be used as a fast and economically acceptable evaluation of antioxidative activity. The advantage of such model is its ability to predict the antioxidative activity of a drug regardless of its structural diversity or therapeutic classification.