Persistence of immunity to SARS-CoV-2 over time in the ski resort Ischgl.

Background In early March 2020, a SARS-CoV-2 outbreak in the ski resort Ischgl in Austria triggered the spread of SARS-CoV-2 throughout Austria and Northern Europe. In a previous study, we found that the seroprevalence in the adult population of Ischgl had reached 45% by the end of April, representing an exceptionally high level of local seropositivity in Europe. We performed a follow-up study in Ischgl, which is the first to show persistence of immunity and protection against SARS-CoV-2 and some of its variants at a community level. Methods Of the 1259 adults that participated in the baseline study, 801 have been included in the follow-up in November 2020. The study involved the analysis of binding and neutralizing antibodies and T cell responses. In addition, the incidence of SARS-CoV-2 and its variants in Ischgl was compared to the incidence in similar municipalities in Tyrol until April 2021. Findings For the 801 individuals that participated in both studies, the seroprevalence declined from 51.4% (95% confidence interval (CI) 47.9-54.9) to 45.4% (95% CI 42.0-49.0). Median antibody concentrations dropped considerably (5.345, 95% CI 4.833 - 6.123 to 2.298, 95% CI 2.141 - 2.527) but antibody avidity increased (17.02, 95% CI 16.49 - 17.94 to 42.46, 95% CI 41.06 - 46.26). Only one person had lost detectable antibodies and T cell responses. In parallel to this persistent immunity, we observed that Ischgl was relatively spared, compared to similar municipalities, from the prominent second COVID-19 wave that hit Austria in November 2020. In addition, we used sequencing data to show that the local immunity acquired from wild-type infections also helped to curb infections from variants of SARS-CoV-2 which spread in Austria since January 2021. Interpretation The relatively high level of seroprevalence (40-45%) in Ischgl persisted and might have been associated with the observed protection of Ischgl residents against virus infection during the second COVID-19 wave as well as against variant spread in 2021. Funding Funding was provided by the government of Tyrol and the FWF Austrian Science Fund.

Marked Increase in Avidity of SARS-CoV-2 Antibodies 7-8 Months After Infection Is Not Diminished in Old Age.

The kinetics of immunoglobulin G (IgG) avidity maturation during severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection obtained from 217 participants of the Ischgl cohort, Austria, was studied 0.5-1.5 months (baseline) and 7-8 months (follow-up) after infection. The IgG avidity assay, using a modified IgG enzyme-linked immunosorbent assay (ELISA) and 5.5 M urea, revealed that old age does not diminish the increase in avidity, detected in all participants positive at both time points, from 18% to 42%. High avidity was associated with a marked residual neutralization capacity in 97.2.% of participants (211/217), which was even higher in the older age group, revealing an important role of avidity assays as easy and cheap surrogate tests for assessing the maturation of the immune system conveying potential protection against further SARS-CoV-2 infections without necessitating expensive and laborious neutralization assays.

High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks.

Patients with advanced Parkinson's disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively evaluated every minute. We acquired a dataset of 8,661 minutes of IMU data from 30 patients, with annotations about the motor state (OFF,ON, DYSKINETIC) based on MDS-UPDRS global bradykinesia item and the AIMS upper limb dyskinesia item. Using a 1-minute window size as an input for a convolutional neural network trained on data from a subset of patients, we achieved a three-class balanced accuracy of 0.654 on data from previously unseen subjects. This corresponds to detecting the OFF, ON, or DYSKINETIC motor state at a sensitivity/specificity of 0.64/0.89, 0.67/0.67 and 0.64/0.89, respectively. On average, the model outputs were highly correlated with the annotation on a per subject scale (r = 0.83/0.84; p < 0.0001), and sustained so for the highly resolved time windows of 1 minute (r = 0.64/0.70; p < 0.0001). Thus, we demonstrate the feasibility of long-term motor-state detection in a free-living setting with deep learning using motion data from a single IMU.

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease.

The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show that the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step toward continuous monitoring of PD in the home environment.