No abstract

This study aimed at identifying human neural proteins that can be attacked by cross-reacting SARS-COV-2 antibodies causing Guillain-Barré syndrome. These markers can be used for the diagnosis of Guillain-Barré syndrome (GBS). To achieve this goal, proteins implicated in the development of GBS were retrieved from literature. These human proteins were compared to SARS-COV-2 surface proteins to identify homologous sequences using Blastp. Then, MHC-I and MHC-II epitopes were determined in the homologous sequences and used for further analysis. Similar human and SARS-COV-2 epitopes were docked to the corresponding MHC molecule to compare the binding pattern of human and SARS-COV-2 proteins to the MHC molecule. Neural cell adhesion molecule is the only neural protein that showed homologous sequence to SARS-COV-2 envelope protein. The homologous sequence was part of HLA-A68 and HLA-DQA/HLA-DQB epitopes had a similar binding pattern to SARS-COV-2 envelope protein. Based on these results, the study suggests that NCAM may play a significant role in the immunopathogenesis of GBS. NCAM antibodies can be used as a marker for Guillain-Barré syndrome. However, more experimental studies are needed to prove these results.

Zika virus infection in humans has been linked to severe neurological sequels and foetal malformations. The rapidly evolving epidemics and serious complications made the frequent updates of Zika virus mandatory. Web search query has emerged as a low-cost real-time surveillance system to anticipate infectious diseases' outbreaks. Hence, we developed a prediction model that could predict Zika-confirmed cases based on Zika search volume in Google Trends. We extracted weekly confirmed Zika cases of two epidemic countries, Brazil and Colombia. We got the weekly Zika search volume in the two countries from Google Trends. We used standard time-series regression (TSR) to predict the weekly confirmed Zika cases based on the Zika search volume (Zika query). The basis TSR model - using 1-week lag of Zika query and using 1-week lag of Zika cases as a control for autocorrelation - was the best for predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time in the prediction. Our results showed that we could use Google search queries to predict Zika cases 1 week earlier before the outbreak. These findings are important to help healthcare authorities evaluate the outbreak and take necessary precautions.

No abstract

No abstract