Impact of immunosuppression on the immune response to SARS‐CoV‐2 infection: A mechanistic study
Bibliographic record
Abstract
The optimal management of immunosuppression in transplant patients infected with COVID-19 is unknown. We performed an in vitro study to determine the effect of individual immunosuppressive agents on SARS-CoV-2-specific T-cell cytokine expression. Convalescent peripheral blood mononuclear cells from eleven non-immunosuppressed patients with COVID-19 were preincubated with clinically relevant concentrations of immunosuppressive drugs (tacrolimus, mycophenolate, sirolimus, prednisone) and then stimulated with a SARS-CoV-2 peptide pool. Supernatants were analyzed by 14-plex high sensitivity T-cell cytokine array. With increasing concentrations of tacrolimus, there was a trend to reduction in the release of IL-2 (p = .0137), and IFN-γ (p = .0147) in response to peptide stimulation. There was also a subsequent trend toward a Th2 phenotype, indicated by lower IFN-γ:IL-13 ratio (p = .0663) and IFNγ:IL-4 ratio (p = .0176). Sirolimus appeared to be associated with a proinflammatory cytokine release, including TNF-α (p = .0027) and IL-1β (p = .0016), in response to SARS-CoV-2 peptides. In contrast, mycophenolate and prednisone did not influence the SARS-CoV-2-specific cytokine response. These are preliminary findings only, with larger studies required to inform clinical recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".