Persistent chemosensory dysfunction in a young patient with mild COVID-19 with partial recovery 15 months after the onset.

OBJECTIVE: It is reported that recovery from COVID-19 chemosensory deficit generally occurs in a few weeks, although olfactory dysfunction may persist longer. Here, we provide a detailed follow-up clinical investigation in a very young female patient (17-year-old) with a long-lasting anosmia after a mild infection, with partial recovery 15 months after the onset. METHODS: Neuroimaging and neurophysiologic assessments as well as olfactory mucosa swabbing for microbiological and immunocytochemical analyses were performed. Olfactory and gustatory evaluations were conducted through validated tests. RESULTS: Chemosensory evaluations were consistent with anosmia associated with parosmia phenomena and gustatory impairment, the latter less persistent. Brain MRI (3.0 T) showed no microvascular injury in olfactory bulbs and brain albeit we cannot rule out slight structural abnormalities during the acute phase, and a high-density EEG was negative. Immunocytochemistry of olfactory mucosa swabs showed high expression of ACE2 in sustentacular cells and lower dot-like cytoplasmic positivity in neuronal-shaped cells. DISCUSSION: The occurrence of long-term persistent olfactory deficit in spite of the absence of structural brain and olfactory bulb involvement supports the view of a possible persistent dysfunction of both sustentacular cells and olfactory neurons. The gustatory dysfunction even if less persisting for the described features could be related to a primary gustatory system involvement. Future longitudinal studies are needed to investigate the persistence of chemosensory impairment, which could have a relevant impact on the daily life.

CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.

PURPOSE: To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. MATERIAL AND METHODS: CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C-), respectively. C- patients, however, presented with interstitial lung involvement. A subgroup of C-, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. RESULTS: The first model classified C + and C- pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C- (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). CONCLUSION: Whole lung ML models based on radiomics can classify C + and C- interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.