Hydroxychloroquine reduces T cells activation recall antigen responses
Bibliographic record
Abstract
Background In the context of the current COVID-19 pandemic, there is still limited information about how people suffering from autoimmune diseases respond to the different COVID vaccines. The fact that they are taking an immunosuppressant or other drugs that aim to decrease the immune system activities, such as hydroxychloroquine (HCQ), could also impact their ability to respond to a COVID vaccine and vaccines in general. Methods Heathy donors were given 200mg of HCQ daily for 6-weeks to assess HCQs impact on the systemic T cells and humoral immune response. Peripheral blood mononuclear cells (PBMC) and plasma were obtained at baseline and 6-weeks after starting daily HCQ. Flow cytometry assays were designed to determine changes in T cell activation and T cell responses. Bead array multiplex were used to analyse antibodies and cytokine levels before and after HCQ intake. Results As anticipated, HCQ treatment decreased ex vivo T cell activation. We observed a decrease in CD4 + CD161 - expressing CCR5 (p = 0.015) and CD69 (p = 0.004) as well as in CD8 + CCR5 + (p = 0.003), CD8 + CD161 + CCR5 + (p = 0.002) and CD8 + CD161 + CD95 + (p = 0.004). Additionally, HCQ decreased the proportion of Th17 expressing CD29 (p = 0.019), a subset associated with persistent inflammation. The proportion of T regulatory cells expressing the inhibitory molecule TIGIT was also reduced by HCQ (p = 0.003). As well, T cells from people on HCQ were less responsive to activation and cytokine production following stimulation with recall antigens and memory T cells were less likely to produce both IFNγ and TNFα following stimulation. Conclusion This study shows HCQ is associated with lower T cell activation and decreased T cell cytokine production. While this study was not performed with the intent of looking at COVID vaccine response, it does provide important information about the changes in immune response that may occur in patient taking HCQ as a treatment for their autoimmune disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".