Monitoring of Polyomavirus BK Virus Viruria and Viremia in Renal Allograft Recipients by Use of a Quantitative Real-Time PCR Assay: One-Year Prospective Study
Bibliographic record
Abstract
We have developed a real-time quantitative PCR (rt-QPCR) assay to detect and kinetically monitor BK virus viruria and viremia in renal transplant recipients (RTRs). A total of 607 urine and 223 plasma samples were collected from 203 individuals including those with BK virus-associated nephropathy (BKVAN) (n = 8), those undergoing routine posttransplant surveillance (SV) (n = 155), those with nontransplant chronic kidney disease (NT-CKD) (n = 20), and healthy living kidney donors (LD) (n = 20). The rt-QPCR assay was found to be highly sensitive and specific, with a wide dynamic range (2.4 to 11 log(10) copies/ml) and very good precision (coefficient of variation, approximately 5.9%). There was a significant difference in the prevalences of viruria and viremia between the BKVAN (100% and 100%) and SV (23% and 3.9%) groups (P < 0.001). No viruria or viremia was detected in LD or in NT-CKD patients. The median (range) peak levels of BK virus viruria and viremia, in log(10) copies/ml, were 10.26 (9.04 to 10.83) and 4.83 (3.65 to 5.86) for the BKVAN group versus 0 (0 to 10.83) and 0 (0 to 5.65) for the SV group, respectively (P < 0.001). When the BK virus load in the urine was <7.0 log(10) copies/ml, no BK virus viremia was detected. When the BK virus load in the urine reached 7.0, 8.0, 9.0, and > or =10.0 log(10) copies/ml, the corresponding detection of BK virus viremia increased to 20, 33, 50, and 100%, respectively. We propose monitoring of BK virus viruria in RTRs, with plasma BK virus load testing reserved for those with viruria levels of > or =7.0 log(10) copies/ml.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".