Molecular predictors for anaemia after kidney transplantation
Bibliographic record
Abstract
BACKGROUND: Anaemia of chronic kidney disease is a well-studied comorbidity, but the molecular predictors of post-transplant anaemia remain elusive. METHODS: In this case-control study, 25 subjects with post-transplant anaemia, defined as erythropoiesis-stimulating agent (ESA) requirement within the first post-transplant year, were matched to 25 control recipients with comparable demographics but no anaemia using the Austrian Dialysis and Transplant Registry. Genome-wide gene expression analyses of deceased donor kidney biopsies obtained immediately before engraftment were performed using custom cDNA microarrays. Significant molecular features were included together with clinical variables in a multivariable logistic regression analysis and further analysed with respect to their molecular functions, biological processes and cellular locations using gene ontology terms and protein-protein interactions. RESULTS: Immunity response molecules were over-represented in the up-regulated gene list suggesting the involvement of the inflammation cascade as a predictor of ESA requirement after engraftment. From the initial list of the 34 differentially expressed genes, we identified the best three genes predicting ESA requirement in the first year by a stepwise gene selection algorithm. SPRR2C (OR = 0.24, 95% CI 0.07-0.85, P = 0.027) and GSTT1 (OR = 2.40, 95% CI 1.21-4.77, P = 0.013) remained significant after adjusting for donor age, eGFR, BCAR and CRP. CONCLUSION: In summary, we identified three biomarkers (SPRR2C, B3GALTL and GSTT1) of post-transplant anaemia in donor kidney biopsies that correctly predicted ESA requirement within the first year after transplantation in 93% of the cases.
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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 | 0.001 | 0.001 |
| 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".