Strategies to prevent BK virus infection in kidney transplant recipients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite improvements in posttransplant care, BK virus (BKV) remains one of the most challenging posttransplant infections in kidney transplant recipients with high rates of allograft failure. In the absence of well tolerated and efficacious viral specific therapeutics, treatment is primarily focused on reduction of immunosuppression, which poses a risk of rejection and fails to lead to viral clearance in a number of patients. RECENT FINDINGS: Recent work has turned toward preventive therapies analogous to those used for other infections like cytomegalovirus. These efforts have focused on the use of quinolone antibiotic prophylaxis to prevent BKV infection and pretransplant vaccination to boost humoral and cellular immunity. SUMMARY: Despite promising in-vitro and observational data, quinolone antibiotic prophylaxis has not been effective in preventing BKV infection in prospective studies. However, prophylaxis with newer less toxic viral specific agents such as brincidofovir - the lipid oral formulation of cidofovir - may yet prove effective. Strategies focused on eliciting a humoral immune response to recombinant virus-like particles or using adoptive transfer of BKV-specific T cells have also shown significant potential to prevent BKV infection in organ transplant recipients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".