Circulating cytokine and chemokine patterns associated with cytomegalovirus reactivation after stem cell transplantation
Bibliographic record
Abstract
Abstract Objectives Human cytomegalovirus (HCMV) reactivation is the leading viral complication after allogeneic haematopoietic stem cell transplantation (allo‐HSCT). Understanding of circulating cytokine/chemokine patterns which accompany HCMV reactivation and correlate with HCMV DNAemia magnitude is limited. We aimed to characterise plasma cytokine/chemokine profiles in 36 allo‐HSCT patients (21 with HCMV reactivation and 15 without HCMV reactivation) at four time‐points in the first 100‐day post‐transplant. Methods The concentrations of 31 cytokines/chemokines in plasma samples were analysed using a multiplex bead‐based immunoassay. Cytokine/chemokine concentrations were compared in patients with high‐level HCMV DNAemia, low‐level HCMV DNAemia or no HCMV reactivation, and correlated with immune cell frequencies measured using mass cytometry. Results Increased plasma levels of T helper 1‐type cytokines/chemokines (TNF, IL‐18, IP‐10, MIG) were detected in patients with HCMV reactivation at the peak of HCMV DNAemia, relative to non‐reactivators. Stem cell factor (SCF) levels were significantly higher before the detection of HCMV reactivation in patients who went on to develop high‐level HCMV DNAemia (810–52 740 copies/mL) vs. low‐level HCMV DNAemia (< 250 copies/mL). High‐level HCMV reactivators, but not low‐level reactivators, developed an elevated inflammatory cytokine/chemokine profile (MIP‐1α, MIP‐1β, TNF, LT‐α, IL‐13, IL‐9, SCF, HGF) at the peak of reactivation. Plasma cytokine concentrations displayed unique correlations with circulating immune cell frequencies in patients with HCMV reactivation. Conclusion This study identifies distinct circulating cytokine/chemokine signatures associated with the magnitude of HCMV DNAemia and the progression of HCMV reactivation after allo‐HSCT, providing important insight into immune recovery patterns associated with HCMV reactivation and viral control.
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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 | 0.001 |
| 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".