B‐cell‐activating factor code and human cytomegalovirus infection in renal transplant recipients
Bibliographic record
Abstract
The objective of the present study was to explore the correlation between the BAFF signal and HCMV-TLR activation in RTx recipients complicated by HCMV. Peripheral blood (anticoagulated by EDTA-Na2 ) and urine of 113 RTx recipients were collected; healthy volunteers were controlled. Urine HCMV-DNA was detected by real-time PCR. Recipients were classified into a positive group (>10,000 copies/mL urine) and a negative group (<10,000 copies/mL urine). ELISA results showed that sBAFF, sera anti-HCMV pp65 immunoglobulin (Ig)G antibody, and total IgG all significantly increased in recipients with positive HCMV-DNA (>10,000 copies/mL urine) (P < 0.05) compared with negative recipients (<10,000 copies/mL urine). In the positive group, HCMV-DNA copies and total IgG positively correlated with sBAFF (r = 0.988 and 0.625, respectively) (P < 0.05). Luminex assay results suggested that the incidence of anti-HLA I and II and MICA antibody obviously increased in positive recipients. The expression level of BAFF and BAFF-R increased in positive recipients. A total of 88 particular genes-involved in TLR signaling pathways, NF-κB signaling pathways, and cytokine-cytokine receptor signaling pathways-were detected in real-time PCR chip assay. A total of 46 genes were differentially expressed greater than two-fold, and the expression characteristic of BAFF-R was concordant with FACS results. Our findings are that activation of HCMV would induce or enhance the activation of BAFF code in RTx recipients, which may independently or cooperatively participate in renal allograft injury and decrease the long-term outcome of renal allografts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".