Improving our mechanistic understanding of the indirect effects of CMV infection in transplant recipients
Bibliographic record
Abstract
Cytomegalovirus (CMV) is an immunomodulatory virus that indirectly increases the risk for bacterial, fungal, and viral infections. However, the pathogenesis of this phenomenon is poorly understood. We determined whether inflammatory responses to different Toll-like receptor (TLR) ligands are blunted during CMV infection in solid-organ transplant (SOT) patients. Peripheral blood mononuclear cells from 38 SOT patients with and without CMV were incubated in the presence of various viral, fungal, and bacterial TLR ligands. Cytokines were measured in the supernatant by multiplex enzyme-linked immunosorbent assay. Patients had blunted cytokine responses to bacterial, fungal, and viral ligands during CMV infection when compared to the absence of CMV infection. This was independent of viral load, clinical presentation of CMV infection or immunosuppression, supporting the clinical observation in SOT recipients that CMV infection increases susceptibility to bacterial, fungal, and other viral infections. Moreover, in the absence of CMV infection, patients with subsequent CMV infection had lower cytokines in response to TLR ligands compared to those without subsequent CMV infection, suggesting that inherent differences in patients not directly related to CMV also contribute to this increased susceptibility. In summary, these data provide novel ex vivo evidence to support indirect effects of CMV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".