MODELING OF ANTIBODY RESPONSE TO COVID-19 VACCINATIONS IN PATIENTS WITH RHEUMATOID ARTHRITIS

BackgroundUnderstanding the dynamics of humoral immunity after COVID-19 vaccination is crucial in developing vaccination strategies. Antibody response patterns are more complex in patients with rheumatoid arthritis (RA) because of their underlying autoimmunity and immunosuppressive medications. The kinetics of vaccine response in RA patients are not well understood.ObjectivesTo construct a model of antibody response to COVID-19 vaccination in patients with RA.MethodsTwo patient groups were included for the study. The first group was composed of RA patients who were enrolled for influenza vaccination study between Oct 6, 2021 and November 3, 2021, in whom serial serum samples were obtained 0, 4, 16 weeks after vaccination. The second group was consecutively enrolled from outpatient clinic between October 6, 2021 and June 3, 2022, in whom serum sample was obtained once. After collecting data on demographics, vaccination and infection history of COVID-19 were obtained by self-report via questionnaire and data from Korean center for disease control. We then measured antibody titers against receptor binding domain of spike protein (anti-RBD) and nucleocapsid (anti-N), using Chemiluminescence microparticle immunosaasy (Abbott, USA) and Electrochemiluminescence immunoassay (Roche, Germany) respectively. The anti-RBD titer was log-transformed to improve normality. Time from vaccination and log of anti-RBD titer was modeled using fractional polynomial. Covariates including age, sex, BMI, underlying disease and immunosuppressive drugs were analyzed using Generalized Estimating Equations to account for repeated measured from a subject.ResultsA total of 736 patients (1042 samples) were enrolled. After excluding patients who experienced COVID-19 infection before sampling (n=84), those unvaccinated (n=44) and uncertain COVID-19 infection history (n=59), the data on 778 samples from 549 patients were analyzed (Group 1: 125, Group 2: 424). Antibody titer reached peak at 12 days after vaccination and decreased exponentially (Figure 1) which fell to 36.5% from peak after 2 months. Compared to the first vaccination, the 3rd and 4th vaccination significantly shifted anti-RBD antibody response curve (28 times, 95% CI 4~195;32 times 95% CI 4~234, respectively). However, there was no significant shift after the 4th vaccination from the 3rd vaccination (p=0.6405). Multivariable analysis showed that number of vaccinations and sulfasalazine (coefficient: 0.40, 95% CI 0.12~0.68) increased vaccine response but age (coefficient: -0.03, 95% CI -0.04~-0.02), abatacept (coefficient: -2.07, 95% CI -3.30~-0.84) and, JAK inhibitor (coefficient: -0.82, 95% CI -1.34~-0.31) decreased vaccine response.ConclusionAnti-RBD response to COVID-19 vaccination showed a peak at 12 days after vaccination and then exponentially decreased in patient with RA. The antibody response is affected by age and medications used for the treatment of RA.Table 1.ln[RBD (U/ml)]coefficient (univariable)95% CIp-valuecoefficient (multivariable)95% CIp-valuesex (female)0.17-0.22, 0.550.393---age-0.02-0.03, -0.01<.001**-0.03-0.04, -0.02<.001**DM0.11-0.27, 0.500.568---HTN-0.38-0.69, -0.070.018*---CKD0.680.07, 1.290.030*---RA duration (yr)-0.04-0.06, -0.010.001**---Pd (mg/d)-0.06-0.11, 0.000.035*---MTX use-0.23-0.52, 0.050.105---HCQ use0.01-0.28, 0.290.965---SSZ use0.450.07, 0.840.022*0.400.12,0.680.005**LEF use0.00-0.37, 0.370.988---TNF inhibitors use0.29-0.16, 0.730.208---Abatacept use-2.07-3.14, -0.99<.001**-2.07-3.30, -0.840.001**JAK inhibitors use-0.88-1.52, -0.240.007**-0.82-1.34, -0.310.002**Time (months)log(t)-1.96-2.37, -1.54<.001**-1.90-2.29, -1.50<.001**t

Evaluation of humoral immune response after ChAdOx1 nCoV-19 vaccination among health care workers

We assessed the immunogenicity of ChAdOx1 nCoV-19 vaccination by evaluating the levels of SARS-CoV-2 IgG after vaccination and investigated the effect of diverse factors such as gender, age, and adverse reactions after vaccination. The study included a total of 1028 serum samples from 452 healthcare workers. SARS-CoV-2 IgG levels were assessed using the SARS-CoV-2 IgG II Quant assay. Participants completed a questionnaire regarding the intensity and duration of adverse reactions after vaccination. The seropositive rates after the first and second doses were 95.5% and 100%, respectively. The median antibody levels after the second dose showed a 4.2-fold increase compared with the first. Five months after the second dose, the median antibody levels decreased by 3.5-fold. The antibody levels in men were lower than those in women after the first dose and were higher after the second dose. There was no difference according to age groups after the first dose, but after the second dose, in subjects aged 50 and above, the rise in antibody levels was less than that in other age groups. The antibody levels among participants with moderate or severe symptoms were significantly higher than those among participants with mild symptoms after the first dose. There were no statistically significant differences according to the duration of symptoms. We could assume that different age groups and genders might have different immunogenicity following vaccination. The intensity of adverse symptoms was positively correlated with the antibody levels, implying that higher immunogenicity is related to the intensity of adverse symptoms after vaccination. © 2023 The Scandinavian Foundation for Immunology.

A Study on Traffic Prediction for the Backbone of Korea's Research and Science Network Using Machine Learning

To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers' traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.

The Prediction of COVID-19 Using LSTM Algorithms

As COVID-19 enters the pandemic stage, the resulting infections, deaths and economic shocks are emerging. To minimize anxiety and uncertainty about socio-economic damage caused by the COVID-19 pandemic, it is necessary to reasonably predict the economic impact of future disease trends by scientific means. Based on previous cases of epidemic (such as influenza) and economic trends, this study has established an epidemic disease spread model and economic situation prediction model. Based on this model, the author also predict the economic impact of future COVID-19 spread. The results of this study are as follows. First, the deep learning-based economic impact prediction model, which was built based on historical infectious disease data, was verified with verification data to ensure 77% accuracy in predicting inflation rates. Second, based on the economic impact prediction model of the deep learning-based infectious disease, the author presented the COVID-19 trend and future economic impact prediction results for the next 1 year. Currently, most of the published studies on COVID-19 are on the prediction of disease spread by statistical mathematical calculations. This study is expected to be used as an empirical reference to efficient and preemptive decision making by predicting the spread of diseases and economic conditions related to COVID-19 using deep learning technology and historical infectious disease data. © 2021 The Authors. Published by Atlantis Press B.V.

Environmental contamination in the isolation rooms of COVID-19 patients with severe pneumonia requiring mechanical ventilation or high-flow oxygen therapy.

BACKGROUND: Identifying the extent of environmental contamination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for infection control and prevention. The extent of environmental contamination has not been fully investigated in the context of severe coronavirus disease (COVID-19) patients. AIM: To investigate environmental SARS-CoV-2 contamination in the isolation rooms of severe COVID-19 patients requiring mechanical ventilation or high-flow oxygen therapy. METHODS: Environmental swab samples and air samples were collected from the isolation rooms of three COVID-19 patients with severe pneumonia. Patients 1 and 2 received mechanical ventilation with a closed suction system, while patient 3 received high-flow oxygen therapy and non-invasive ventilation. Real-time reverse transcription-polymerase chain reaction (rRT-PCR) was used to detect SARS-CoV-2; viral cultures were performed for samples not negative on rRT-PCR. FINDINGS: Of the 48 swab samples collected in the rooms of patients 1 and 2, only samples from the outside surfaces of the endotracheal tubes tested positive for SARS-CoV-2 by rRT-PCR. However, in patient 3's room, 13 of the 28 environmental samples (fomites, fixed structures, and ventilation exit on the ceiling) showed positive results. Air samples were negative for SARS-CoV-2. Viable viruses were identified on the surface of the endotracheal tube of patient 1 and seven sites in patient 3's room. CONCLUSION: Environmental contamination of SARS-CoV-2 may be a route of viral transmission. However, it might be minimized when patients receive mechanical ventilation with a closed suction system. These findings can provide evidence for guidelines for the safe use of personal protective equipment.