Necroptosis in Immunity and Ischemia-Reperfusion Injury
Bibliographic record
Abstract
Transplantation is invariably associated with ischemia-reperfusion injury (IRI), inflammation and rejection. Resultant cell death has morphological features of necrosis but programmed cell death has been synonymous with apoptosis until pathways of regulated necrosis (RN) have been described. The best-studied RN pathway, necroptosis, is triggered by perturbation of caspase-8-mediated apoptosis and depends on receptor-interacting protein kinases 1 and 3 (RIPK1/RIPK3) as well as mixed linage kinase domain like to form the necroptosome. The release of cytosolic content and cell death-associated molecular patterns (CDAMPs) can trigger innate and promote adaptive immune responses. Thus, the form of cell death can substantially influence alloimmunity and graft survival. Necroptosis is a key element of IRI, and RIPK1 interference by RN-specific inhibitors such as necrostatin-1 protects from IRI in kidney, heart and brain. Necroptosis may be a general mechanism in response to other forms of inflammatory organ injury, and will likely emerge as a promising target in solid organ transplantation. As second-generation RIPK1 and RIPK3 inhibitors become available, clinical trials for the prevention of delayed graft function and attenuation of allograft rejection-mediated injury will emerge. These efforts will accelerate upon further identification of critical necroptosis-triggering receptor(s).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".