Background and purpose
Core clinical manifestations of COVID‐19 include influenza‐like and respiratory symptoms. However, it is now evident that neurological involvement may occur during SARS‐CoV‐2 infection, covering an extensive spectrum of phenotypical manifestations. A major challenge arising from this pandemic is represented by detecting emerging neurological complications following recovery from SARS‐CoV‐2 infection. To date, a few post‐COVID‐19‐infected subjects diagnosed with Parkinson disease (PD) have been described, raising the possibility of a connection between the infection and neurodegenerative processes. Here, we describe a case series of six subjects who developed PD after COVID‐19.
Methods
Patients were observed at Scientific Institute for Research and Health Care Mondino Foundation Hospital, Pavia (Italy), and San Paolo University Hospital of Milan (Italy) between March 2021 and June 2022. In all subjects, SARS‐CoV‐2 infection was confirmed by means of reverse transcriptase polymerase chain reaction from a nasopharyngeal swab. Subjects underwent an accurate neurological evaluation, and neuroimaging studies were performed.
Results
We describe six subjects who developed PD with an average time window after SARS‐CoV‐2 infection of 4–7 weeks. Apparently, no relationship with COVID‐19 severity emerged, and no overt structural brain abnormalities were found. All subjects experienced unilateral resting tremor at onset and showed a satisfactory response to dopaminergic treatment.
Conclusions
Immune responses to SARS‐CoV‐2 infection have been shown to shape the individual susceptibility to develop long‐term consequences. We hypothesize that, in these subjects, COVID‐19 has unmasked a latent neurodegenerative process. Characterization of the neuroinflammatory signatures in larger cohorts is warranted, which might provide novel insights into the pathogenesis of PD.
Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.
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