Donor kidney injury molecule-1 promotes graft recovery by regulating systemic necroinflammation
Bibliographic record
Abstract
Ischemia-reperfusion injury during kidney transplantation predisposes to delayed graft function, rejection, and premature graft failure. Exacerbation of tissue damage and alloimmune responses may be explained by necroinflammation: an autoamplification loop of cell death and inflammation, which is mediated by the release of damage-associated molecular patterns (eg, high-mobility group box-1; HMGB1) from necrotic cells that activate both innate and adaptive immune pathways. Kidney injury molecule-1 (KIM-1) is a phosphatidylserine receptor that is upregulated on injured proximal tubular epithelial cells and enables them to clear apoptotic and necrotic cells. Here we show a pivotal role for clearance of dying cells in regulating necroinflammation in a syngeneic murine kidney transplant model. We found persistent KIM-1 expression in KIM-1 +/+ kidney grafts posttransplantation. Compared to recipients of KIM-1 +/+ kidneys, recipients of KIM-1 −/− kidneys exhibited significantly more renal dysfunction, apoptosis and necrosis, tubular obstruction, and graft failure. KIM-1 −/− grafts also had more inflammatory cytokines, infiltrating neutrophils, and macrophages compared to KIM-1 +/+ grafts. Most significantly, passive release of HMGB1 from apoptotic and necrotic cells led to dramatically higher serum HMGB1 levels and increased proinflammatory macrophages in recipients of KIM-1 −/− grafts. Our data identify an endogenous protective mechanism against necroinflammation in kidney grafts that may be of therapeutic relevance in transplantation.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".