Evidence of necroptosis in hearts subjected to various forms of ischemic insults
Bibliographic record
Abstract
Long-lasting ischemia can result in cell loss; however, repeated episodes of brief ischemia increase the resistance of the heart against deleterious effects of subsequent prolonged ischemic insult and promote cell survival. Traditionally, it is believed that the supply of blood to the ischemic heart is associated with release of cytokines, activation of inflammatory response, and induction of necrotic cell death. In the past few years, this paradigm of passive necrosis as an uncontrolled cell death has been re-examined and the existence of a strictly regulated form of necrotic cell death, necroptosis, has been documented. This controlled cell death modality, resembling all morphological features of necrosis, has been investigated in different types of ischemia-associated heart injuries. The process of necroptosis has been found to be dependent on the activation of RIP1-RIP3-MLKL axis, which induces changes leading to the rupture of cell membrane. This pathway is activated by TNF-α, which has also been implicated in the cardioprotective signaling pathway of ischemic preconditioning. Thus, this review is intended to describe the TNF-α-mediated signaling leading to either cell survival or necroptotic cell death. In addition, some experimental data suggesting a link between heart dysfunction and the cellular loss due to necroptosis are discussed in various conditions of myocardial ischemia.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".