An Assessment of Donor‐to‐Recipient Transmission Patterns of Human Cytomegalovirus by Analysis of Viral Genomic Variants
Bibliographic record
Abstract
BACKGROUND: We studied human cytomegalovirus (CMV) donor-to-recipient transmission patterns in organ transplantation by analyzing genomic variants on the basis of CMV glycoprotein B (gB) genotyping. METHODS: Organ transplant recipients were included in the study if they had CMV viremia, if they had received an organ from a CMV-seropositive donor, and if there was at least 1 other recipient of an organ from the same donor who developed CMV viremia. Genotypes (gB1-4) were determined by real-time polymerase chain reaction. RESULTS: Forty-seven recipients of organs from 21 donors developed CMV viremia. Twenty-three recipients had a pretransplant donor/recipient (D/R) CMV serostatus of D(+)/R(+), and 24 had a serostatus of D(+)/R(-). The prevalences of genotypes in recipients were as follows: for gB1, 51% (n = 24); for gB2, 19% (n = 9); for gB3, 9% (n = 4); for gB4, 0% (n = 0); and for mixed infection, 21% (n = 10). Recipients of an organ from a common donor had infection with CMV of the same gB genotype in 12 (57%) of 21 instances. Concordance between genotypes was higher among seronegative (i.e., D(+)/R(-)) recipients than among seropositive (D(+)/R(+)) recipients, although discordances resulting from the transmission of multiple strains were seen. In seropositive recipients, transmission of multiple strains from the donor could not be differentiated from reactivation of a recipient's own strains. CONCLUSION: Our analysis of strain concordance among recipients of organs from common donors showed that transmission of CMV has complex dynamic patterns. In seropositive recipients, transmission or reactivation of multiple CMV strains is possible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".