Contribution of diminished kidney transplant GFR to increased circulating chemokine ligand 27 level
Bibliographic record
Abstract
BACKGROUND: Inflammatory chemokine ligands (CCLs) play an important role in cardiovascular disease and allograft injury. CCLs may independently associate with diminished estimated glomerular filtration rate (eGFR) in stable renal transplant recipients (RTR). METHODS: Plasma levels of 19 CCLs (1, 2, 3, 4, 5, 8, 11, 13, 15, 17, 21, 24, 26, 27, CXCL5, 8, 10, 12 and 13) were measured in a cohort of 101 RTR. The cohort was divided according to CKD-EPI equation into three groups; group 1: eGFR ≥ 60 ml/min, group 2: eGFR 30-59.9 ml/min and group 3 eGFR ≤ 29.9 ml/min. ANOVA, Krusklwallis, Mann- Whitney Spearman correlation and regression analysis tests were used to determine association between reduced eGFR and inflammatory CCLs plasma levels measured by multiplex techniques. 20 healthy subjects with eGFR above 90 ml/min were included as control. Significance was sat at < 0.05. RESULTS: = 0.001)}. In a second MVA between CCL 27 and patient's demographics and laboratory variables, diminished eGFR, and elevated PTH, out of the twenty one available variables remained significantly associated with elevated CCL27levels. CONCLUSION: Diminished eGFR in stable RTR is associated with elevated plasma levels of CCL27. This association may explain, at least in part, the independent contribution of reduced eGFR to enhanced inflammation in RTR.
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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) | 0.000 | 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 | 0.000 |
| 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".