Pandemic Preparedness: Maintaining Adequate Immune Fitness by Attaining a Normal, Healthy Body Weight.

In addition to developing effective medicines and vaccines, pandemic preparedness also comprises general health-related, behavioral, and psychological aspects related to being more resistant in the case of future pandemics. In the context of the 2019 coronavirus (COVID-19) pandemic, recent research revealed that reduced perceived immune fitness was the best predictor of reporting more frequent and more severe COVID-19 symptoms. Up until now (June 2022), during the COVID-19 pandemic, the majority of patients who have been hospitalized were characterized as being overweight. It is therefore essential to further evaluate the relationship between body mass index (BMI) and immune fitness. This was performed by analyzing pooled data from previously published studies, conducted among N = 8586 Dutch adults. It was hypothesized that attaining a normal, healthy body weight is associated with optimal perceived immune fitness. The analysis revealed that a deviation from normal weight (i.e., having a BMI outside the range of 18.5 to 24.9 kg/m2) was associated with significantly reduced perceived immune fitness, as assessed with the immune status questionnaire and a single item perceived immune fitness scale. The effects were significant for both underweight and overweight groups and most pronounced for the obese groups. The results suggest that attaining a normal, healthy body weight might significantly contribute to maintaining adequate perceived immune fitness. Therefore, attaining a normal body weight might be an essential component of pandemic preparedness and should be supported by creating awareness and promoting the importance of regular exercise and the consumption of healthy food.

Pandemic Preparedness: The Importance of Adequate Immune Fitness.

Pandemic preparedness is an important issue in relation to future pandemics. The two studies described here aimed to identify factors predicting the presence and severity of coronavirus disease 2019 (COVID-19) symptoms. The CLOFIT study comprised an online survey among the Dutch population (n = 1415). Perceived immune fitness before the pandemic (2019) and during the first lockdown period (15 March-11 May 2020) and the number and severity of COVID-19 symptoms were assessed. The COTEST study, conducted between December 2020 and June 2021, replicated the CLOFIT study in n = 925 participants who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Dutch commercial test locations. The CLOFIT study revealed that immune fitness before the pandemic was the greatest predictor of the number and severity of COVID-19 symptoms (20.1% and 19.8%, respectively). Other significant predictors included immune fitness during the lockdown (5.5% and 7.1%, respectively), and having underlying diseases (0.4% and 0.5%, respectively). In the COTEST study, for those who tested positive for SARS-CoV-2, immune fitness before the pandemic was the single predictor of the number (27.2%) and severity (33.1%) of COVID-19 symptoms during the pandemic. In conclusion, for those who tested positive for SARS-CoV-2, immune fitness before the pandemic was the strongest predictor of the number and severity of COVID-19 symptoms during the pandemic. Therefore, the development of strategies to maintain an adequate immune fitness must be regarded as an essential component of pandemic preparedness.

Immune Fitness and the Psychosocial and Health Consequences of the COVID-19 Pandemic Lockdown in The Netherlands: Methodology and Design of the CLOFIT Study.

This article provides an overview of the design and methodology of the "Corona lockdown: how fit are you?" (CLOFIT) study, including the questionnaires and scales that were included in the online survey. The aim of the CLOFIT study was to investigate the psychosocial and health consequences of the coronavirus disease 2019 (COVID-19) pandemic in the Netherlands. The survey was conducted among the Dutch population to collect data on immune fitness and the psychological and health consequences of the 2019 coronavirus disease (COVID-19) pandemic lockdown in the Netherlands. The CLOFIT dataset contains measures from N = 1910 participants and is broadly representative of the Dutch general population. The dataset represents both sexes, a range of ages including the elderly, different education levels, and ethnic backgrounds. The cohort also includes people with a diverse health status and range of medication use.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.