Comparison of the Effect of Standard and Novel Immunosuppressive Drugs on CMV-Specific T-Cell Cytokine Profiling
Bibliographic record
Abstract
BACKGROUND: Data on how different immunosuppressive drugs affect cytomegalovirus (CMV)-specific T-cell responses may help guide more rational modification of immunosuppression in patients with CMV replication. We assessed the in vitro effects of individual standard and novel immunosuppressive drugs on a broad range of CMV-specific T-cell responses. METHODS: Peripheral blood mononuclear cells from healthy CMV-seropositive donors were preincubated with serial dilutions of tacrolimus, mycophenolate (MPA), sirolimus, tofacitinib, and belatacept. CMV-pp65 or CMV-pp72 peptide pools were used for stimulation. CMV-specific cytokine (Th1 and Th2) and chemokine responses were determined (a total of 5400 measurements). P<0.01 was set as significant. RESULTS: After CMV stimulation, dose-dependent suppression of Th1, Th2, and chemokines was seen, but significant differences between drugs were present. For example, tacrolimus was more potent in inhibiting CMV-specific Th1 cytokines versus Th2, whereas MPA preferentially inhibited Th2 cytokines. In a comparison of the relative potency of each drug at different dosing ranges, tacrolimus had the strongest Th1 inhibitory effect (median inhibition of interferon-γ at 97.5%; P=0.004-0.008) followed by sirolimus (median inhibition at 82.4%). The remaining agents (MPA, belatacept, and tofacitinib) had less apparent dose-dependent effects on interferon-γ (belatacept median inhibition at 21.5%; P=0.004 vs. tacrolimus). CONCLUSION: Immunosuppression-specific and dose-dependent reductions in CMV-specific cytokine release were observed with significant differences in Th1 versus Th2 profiles and in relative potency of the drugs.
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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 | 0.001 | 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) | 0.001 | 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".