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Antibody response <t>after</t> <t>SARS-CoV-2</t> vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of <t>glm</t> (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.
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Antibody response <t>after</t> <t>SARS-CoV-2</t> vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of <t>glm</t> (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.
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Antibody response after SARS-CoV-2 vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of glm (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.

Journal: NPJ Vaccines

Article Title: Beneficial effect of temporary methotrexate interruption on B and T cell responses upon SARS-CoV-2 vaccination in patients with rheumatoid arthritis or psoriatic arthritis

doi: 10.1038/s41541-024-00805-3

Figure Lengend Snippet: Antibody response after SARS-CoV-2 vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of glm (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.

Article Snippet: To determine which variables could explain variability in humoral and cellular response to SARS-CoV-2 vaccination, we performed a multivariable linear regression model using generalised linear models (GLM) for each dependent variable by using the Stata glm command.

Techniques: Infection