Epithelial to Mesenchymal Transition During Late Deterioration of Human Kidney Transplants: The Role of Tubular Cells in Fibrogenesis
Bibliographic record
Abstract
The hallmark of failing renal transplants is tubular atrophy and interstitial fibrosis (TA/IF). Injury to tubular epithelial cells (TEC) could contribute to fibrogenesis via epithelial-mesenchymal transition (EMT). We examined the features of EMT in renal transplants that developed TA/IF. Biopsies from 10 allograft kidneys with impaired function and TA/IF and 10 biopsies from transplants with stable function were compared to their implantation biopsies. Relative to implantation biopsies, TEC in TA/IF kidneys showed loss of epithelial markers (E-cadherin, cytokeratin) with altered distribution. Some TEC also showed new cytoplasmic expression of mesenchymal markers vimentin, S100A4, and alpha smooth muscle actin (alpha-SMA) and collagen synthesis marker heat shock protein (HSP-47), both in deteriorating and atrophic tubules. Double immunostaining showed coexpression of cytokeratin and vimentin, S100A4 and HSP-47, indicating intermediate stages of EMT in TA/IF. These changes were absent or much less in transplants with stable function. EMT features in the TA/IF group correlated with serum creatinine (vimentin, S100A4, HSP-47), history of T-cell-mediated rejection (cytokeratin, S100A4) and proteinuria (cytokeratin). These findings support a model in which the TEC damage induces loss of epithelial features and expression of fibroblast features, as a common pathway of deterioration by either immunologic or nonimmunologic processes.
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 | 0.000 | 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) | 0.001 | 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".